# Risk-Based Mass Loading Limits for Solvents in Disposed Wipes and Laundry Sludges Managed in Municipal LandfillsPrepared under contract to the U.S. Environmental Protection Agency, Office of Resource Conservation And Recovery Mail Code 5305P 1200 Pennsylvania Ave. N.W. Washington, D.C. 20460

inter alia , Contract EP-W-09-004, Work Assignment 0-01

by:

RTI International P.O. Box 12194 Research Triangle Park, NC 27709

October 2, 2009[This page intentionally left blank]

## Table of Contents

RISK-BASED MASS LOADING LIMITS FOR SOLVENTS IN DISPOSED WIPES AND LAUNDRY SLUDGES MANAGED IN MUNICIPAL LANDFILLS— EXECUTIVE SUMMARY ...................................................................................................................... 1

Purpose and Scope of the Risk Analysis ....................................................................................................................1

Risk Analysis Methodology

............................................................................................................................... ..........2

Risk-Based Mass Loading Limits

............................................................................................................................... 7

1.0 INTRODUCTION .................................................................................................. 1

1.1 Background

............................................................................................................................... .....................1

1.2 Purpose

............................................................................................................................... ............................1

1.3 Methodology Overview

............................................................................................................................... ..2

1.4 Document Organization

............................................................................................................................... .4

2.0 PROBLEM FORMULATION ................................................................................ 1

2.1 Conceptual Model

............................................................................................................................... ..........2

2.2 Analysis Plan

............................................................................................................................... ...................4

3.0 ANALYSIS ........................................................................................................... 1

3.1 Landfill Source Modeling and Characterization ........................................................................................2 3.1.1 Conceptual Approach

............................................................................................................................... ..2 3.1.2 Model Inputs

............................................................................................................................... ..............11 3.1.3 Source Model Outputs ..............................................................................................................................2 0

3.2 Air Dispersion Modeling .............................................................................................................................28 3.2.1 Conceptual Model

............................................................................................................................... .....28 3.2.2 Model Inputs

............................................................................................................................... ..............29 3.2.3 Model Outputs

............................................................................................................................... ...........31

3.3 Groundwater Modeling ..............................................................................................................................3 1 3.3.1 Conceptual Model

............................................................................................................................... .....31 3.3.2 Model Inputs

............................................................................................................................... ..............41 3.3.3 Model Outputs

............................................................................................................................... ...........42

3.4 Shower Modeling

............................................................................................................................... ..........43 3.4.1 Conceptual Model

............................................................................................................................... .....43 3.4.2 Model Inputs

............................................................................................................................... ..............46 3.4.3 Model Outputs

............................................................................................................................... ...........47

3.5 Exposure Modeling

............................................................................................................................... ......47 3.5.1 Conceptual Model

............................................................................................................................... .....47 3.5.2 Model Inputs

............................................................................................................................... ..............52 3.5.3 Model Outputs

............................................................................................................................... ...........543.6 Human Health Effects Modeling ................................................................................................................54 3.6.1 Conceptual Model

............................................................................................................................... .....54 3.6.2 Model Inputs

............................................................................................................................... ..............59 3.6.3 Model Outputs

............................................................................................................................... ...........61

4.0 RISK CHARACTERIZATION ............................................................................... 1

4.1 Human Health Risk

............................................................................................................................... ........1 4.1.1 Ambient Air

............................................................................................................................... .................2 4.1.2 Groundwater

............................................................................................................................... ................2

4.2 Uncertainty Characterization .....................................................................................................................12 4.2.1 Conceptual Approach

............................................................................................................................... 13 4.2.2 Model Uncertainty

............................................................................................................................... .....16 4.2.3 Parameter Uncertainty ..............................................................................................................................2 3

5.0 LANDFILL MASS LOADING RATES .................................................................. 1

5.1 Summary of Risk Analysis Calculations .....................................................................................................1 Step 1. Run the Source Model Using a Constant Initial Concentration ....................................................................2 Step 2. Normalize the Source Model Results to a Constant Mass Loading Rate ......................................................2 Step 3. Calculate Risks and Noncancer HQs Based on the Unitized Mass Loading Rate ........................................3 Step 4. Derive the Scaling Factors Needed to Calculate the Risk-Based Mass Loading Rates ................................3 Step 5. Calculate the Risk-Based Mass Loading Rates .............................................................................................3 Step 6. Select the Limiting Path way for Selected Risk Percentiles ..........................................................................4 Step 7. Check Whether the Mass Loading Li mits Exceed Solvent Pa rtitioning Limitations ....................................4

5.2 Mass Loading Limits

............................................................................................................................... ......4

6.0 REFERENCES ..................................................................................................... 1Review Draft Table of Contents



## List of Figures 2-1 Conceptual model for mode ling solvents in wipes..............................................................2-2 2-2 Modeling scenarios evaluated in deve loping risk-based mass loading rates......................2-4 2-3 How the Monte Carlo framew ork addresses uncertainty....................................................2-7 2-4 Basic Monte Carlo structure................................................................................................ 2-8 2-5 Meteorological stations and region boundaries for th e contiguous 48 states....................2-15 3-1 Model representation of opera tional landfill waste addition...............................................3-4 3-2 Coupled in-series reactor model of a column of landfill cells.............................................3-5 3-3 Measured infiltration (water added) and leachate flow rates over time (from Fungaroli and Steiner, 1979).....................................................................................3-8 3-4 Relationship between the infiltration rate and the gas generation rate..............................3-19 3-5 Schematic diagram of groundw ater modeling scenario.....................................................3-32 3-6 Schematic plan view showing procedur e for determining the downstream location of the receptor well: (a) well locatio n determined using radial distance, R rw , and angle off center, 2 rw ; and (b) well location generated randomly within plume limit........3-35 4-1 Comparison of different numbers of iterati ons for 2-ethoxyethanol (least volatile).........4-15 4-2 Comparison of different numbers of iterati ons for tetrachloroethylen e (most volatile)....4-16



## List of Tables ES-1 RB-MLLs Based on an HQ of 1 for 15 So lvents Analyzed Only as Noncarcinogens (kg/year)...................................................................................................................... ...... ES-8 ES-2 RB-MLLs Based on a Cancer Risk Level of 10 -4 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year) ....................................... ES-9 ES-3 RB-MLLs Based on a Cancer Risk Level of 10 -5 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year)........................................ES-9 ES-4 RB-MLLs Based on a Cancer Risk Level of 10 -6 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year)........................................ES-9 2-1 Exposure Model Outputs by Receptor and Exposure Route.............................................2-10 2-2 Outputs of the Risk Model by Receptor and Pathway......................................................2-11 2-3 Hydrogeologic Environm ents in EPACMTP....................................................................2-14 3-1 Characteristics of EPACMTP Soils..................................................................................3-14 3-2 Comparison of Constructed Da ta Set with Other Sources................................................3-17 3-3 Relationship Between the Infiltration Rate and the Gas Generation Rate........................3-19 3-4 Process Summary Results for All Solvents......................................................................3-21 3-5 Percent Contributions of Primary Loss Mechanisms in the Landfill...............................3-23 3-6 Percent Mass Loss from the Landfill to the Atmosphere and Groundwater....................3-25 3-7 Percent Mass Loss from the Landfill Due to No-Cover Diffusion and Liner Leakage...3-26 3-8 Groundwater Pathway DAF Eff 10 ......................................................................................3-39 3-9 Groundwater Pathway DAF Eff 50 .......................................................................................3-40 3-10 Receptors by Exposure Pathway .....................................................................................3-48 3-11 Human Exposure Factor Input Parameters and Data Sources..........................................3-52 3-12 Chronic Health Benchmarks Used in the Analysis..........................................................3-62 4-1 Summary of 50th and 90th Percentile So lvent Human Risk Results at the Unitized Mass Loading Rate (1 kg/year): Adult Ambient Air Inhalation Pathway.........................4-3

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4-2 Summary of 50th and 90th Percentile Solven t Human Risk Results at the Unitized Mass Loading Rate (1 kg/year): Child Ambient Air Inhalation Pathway.........................4-4 4-3 Summary of 50th Percentile Solvent Wipe s Human Risk Results at the Unitized Mass Loading Rate (1 kg/year): Adult Groundwater Exposures.......................................4-5 4-4 Summary of 90th Percentile Solvent Hu man Risk Results at the Unitized Mass Loading Rate (1 kg/year): Adult Groundwater Exposures................................................4-6 4-5 Summary of 50th Percentile Solvent Huma n Risk Results at the Unitized Mass Loading Rate (1 kg/year): Child Groundwater Exposures.................................................4-7 4-6 Summary of 90th Percentile Solvent Huma n Risk Results at the Unitized Mass Loading Rate (1 kg/year): Child Groundwater Exposures.................................................4-8 4-7 Travel Time Associated with the Peak Exposure Concentration at the Receptor Well....4-10 4-8 Minimum Mass Loading Rates for Air Pathwa y Adjusted to Include Leachate Mass (Composite Liner Scenario)..............................................................................................4-19 5-1 RB-MLLs for Noncarcinogens at the 90th Percentile........................................................5-5 5-2 RB-MLLs for Noncarcinogens at the 50th Percentile........................................................5-6 5-3 RB-MLLs for Carcinogens at the 90th Percentile..............................................................5-7 5-4 RB-MLLs for Carcinogens at the 50th Percentile..............................................................5-8

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## List of Acronyms

3MRA Multimedia, Multipathway, Multireceptor Risk Assessment ADD average daily dose API American Petroleum Institute ATSDR Agency for Toxic Substances and Disease Registry CalEPA California Environmental Protection Agency CASRN Chemical Abstract Service Registry Number CCL clay composite lined CDF cumulative distribution function CI confidence interval CSF cancer slope factor CV coefficient of variation DAD dermal absorbed dose DAF dilution and attenuation factor EFH Exposure Factors Handbook EMLR estimated mass loading rate EMRAD Economics, Methods, and Risk Analysis Division (U.S. EPA) EPA Environmental Protection Agency EPACMTP Composite Model for Leachate Mi gration with Transformation Products GCL geosynthetic clay lined GM geomembrane GSCM General Soil Column Model HEAST Health Effects Assessment Summary Tables HELP Hydrologic Evaluation of Landfill Performance HGDB Hydrogeologic Database HHW household hazardous waste HQ hazard quotient HSDB Hazardous Substances Data Bank IRIS Integrated Risk Information System ISCST3 Industrial Source Comp lex – Short Term, Version 3 IWAIR Industrial Waste Air LADD lifetime average daily dose LandGEM Landfill Gas Emissions Model LDS leak detection system LFCR landfill coupled reactor LMOP Landfill Methane Outreach Program MLR mass loading rate MOCLA Model for Organic Compounds in Landfills MRL minimal risk level MSWLF municipal solid waste landfill NCEA National Center for Environmental Assessment NOAEL no observed adverse effects level OMB Office of Management and Budget OSWER Office of Solid Waste and Emergency Response PPRTV Provisional Peer Reviewed Toxicity Value

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RB-MLL risk-based mass loading limit RCRA Resource Conservati on and Recovery Act REL reference exposure level RfC reference concentration RfD reference dose SAB Science Advisory Board (U.S. EPA) SCDM Superfund Chemical Data Matrix UAC unit air concentration URE unit risk estimate URF unit risk factor VOC volatile organic compound WBJ Waste Business Journal WMU waste management unit

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## Risk-Based Mass Loading Limits for Solvents in Disposed Wipes and Laundry Sludges Managed in Municipal Landfills—

Executive Summary

F-Listed Solvents 1,1,2-Trichloroethane 1,2-Dichlorobenzene 2-Ethoxyethanol Acetone Benzene Butanol Chlorobenzene Cresols Cyclohexanone Ethyl acetate Ethyl benzene Isobutanol Methanol The U.S. Environmental Protection Agency (EPA) Office of Solid Waste and Emergency Response (OSWER) is evaluating the management of solvent-contaminated wipes and industrial laundry sludge in “nonhazardous waste landfills.” For this analysis, the term “nonhazardous waste landfills” refers specifically to munici pal solid waste landfills (MSWLFs), which were identified as the disposal de stination for the solvent-contam inated wipes and laundry sludges. The waste from the use of wipes at faci lities across the United States is managed according to existing state a nd federal regulations. Many states currently exclude laundered wipes fro m the definition of solid and/or hazardous waste, while disposed wipe s are regulated as hazardous waste. To support a proposed change in th e regulatory status of disposed solvent-contaminated wipes and laun dry sludges, EPA developed a risk analysis to characterize the potential ri sks associated with the disposal of wipes and laundry sludge in nonhazardous waste landfills. The analysis estimated the amounts of solvents that , if contained on disposed wipes or in laundry sludge, would not exceed the evaluated risk criteria if these materials were disposed of in nonhazardous waste landfills.
Methyl ethyl ketone Methyl isobutyl ketone Methylene chloride Tetrachloroethylene Toluene Trichloroethylene Xylene This report describes the results of a probabilistic ri sk analysis of solvent-contaminated wipes that was used to produce a distribution of risk and hazard estimates with wh ich to calculate a protective mass loading rate for each individual solv ent. A probabilistic approach is particularly appropriate for the deve lopment of risk-based mass loading rates because solvent-contaminated wipes and laundry sludge could be disposed of in landfills of varying geometry and located in many different parts of the country. The probabilistic approach, as implement ed, does not separate sources of uncertainty from sources of variabilit y largely because of the limitations in available data needed to parameterize the model. Thus, th e approach primarily addresses variability in waste management practices, environmenta l settings (e.g., hydrology, meteorology), and exposure-related parameters, su ch as distance from the landf ill and inhalation rates. 

### Purpose and Scope of the Risk Analysis The purpose of this risk analysis is to estimate the amounts of specific F001–F005 listed solvents regulated by the Resource Conservati on Recovery Act (RCRA) (40 CFR 261.31) that can be disposed of in composite lined and un lined MSWLFs without exceeding the evaluated risk criteria at the point of exposure. For this analysis, EPA evaluated both 50 th and 90 th percentile risk criteria to s upport the development of risk-bas ed, solvent-specific mass loading limits for national application. Spec ifically, the criteria stipulate that the al lowable solvent mass from disposed wipes and laundry sludges managed in MSWLFs in the contiguous United States
must

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 Ensure that a selected proporti on (e.g., 90 percent) of the hypot hetical individuals living near a landfill will not be exposed to solvent releases at leve ls of concern. The levels of concern depend on the type of effect:

– For solvents evaluated for carcinogenic effects, the levels of concern are defined as an excess lifetime cancer risk to individuals above 1 chance in 10,000 (i.e., 10 -4 ) through 1 chance in 1,000,000 (i.e., 10 -6 )

– For solvents evaluated for noncancer health eff ects, the level of concern is defined as a ratio of predicted intake levels to safe in take levels—the hazard quotient (HQ)—above 1. Note that an HQ less than or equal to 1 w ould be considered to be below a level of

concern.

EPA evaluated a 90 th percentile risk criterion for the risk-based mass loading limit to be protective of 90 percent of hypothe tically exposed individuals acro ss all of the la ndfill sites in the United States ( Guidance for Risk Characterization , U.S. Environmental Protection Agency, 1995; accessible at http://www.epa.gov/spc/pdfs/rcguide.pdf which states that “For the Agency's purposes, high end risk descriptors are plausible estimates of the in dividual risk for those persons at the upper end of the risk distribution,” or conceptually, individuals with “exposure above about the 90th percentile of the population distribution”). As recommended in the Guidance

, EPA also evaluated the 50th percentile results as the central tendency estimate of that risk distribution. Thus, the “problem statement” for this analys is may be defined as follows: the risk-based mass loading limit (i.e., the amount of mass allowe d to be managed in any MSWLF) should be protective of X percent (i.e., 90) of the hypothe tically exposed individu als across all of the landfill sites in the United States, with the term “p rotective” defined in terms of Y excess risk of cancer (i.e., < 10 -5 ) and Z hazard (i.e., < 1) for noncancer endpoints. To derive the risk-based mass loading lim it (RB-MLL) for the allowable amount of solvent that can be managed in an unlined or composite-lined landfill in any year, the mass loading rate at the 90th percentile of the theoreti cal distribution of risk was adjusted to the risk and/or hazard level specified by the risk criteria. This adjustment or scaling is based on the ratio between the theoretical ri sk/hazard at the 90th pe rcentile and the risk/hazard level specified by the risk criteria for cancer and noncancer effects, respectively. Because the modeling framework is linear with respect to solvent concentration and mass loading, the theoretical 1 distribution of risk is, in effect, a scalable distribution that allows for the estimation of a mass loading limit at any percentile of intere st with regard to poten tial risk/hazard. The resul ting calculation produces a solvent-specific RB-MLL that serves as a cri tical input for EPA’s decision-making process. 

### Risk Analysis Methodology

Problem Formulation. In the problem formulation phase, we developed a risk analysis strategy that reflects the dimensions of the wipes analysis, in cluding spatial and temporal scales (e.g., site-based modeling to support a national app lication); the mechanisms for solvent releases (i.e., long-term low level emissions from landfills); the nature of exposures that can be attributed to solvent releases (e.g., groundw ater ingestion); the types an d location of receptors (e.g.,

1 Theoretical in the sense that the modeling results are based on a unitized, not actual, solvent concentration.

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residents living within 2 km of th e edge of the landfill), and the health endpoints of concern (e.g., cancer risk). The dimensions of the risk analysis—particularly the need to aggregate site-based results for national application—sugge st that variability in parameter values describing the landfills, environmental settings, receptor locations and co ntact rates may be a significant source of uncertainty. To address these sources of variab ility, we implemented a Monte Carlo approach that consisted of 10,000 r ealizations for each scenario, defined by the

 Solvent (e.g., acetone)

 Landfill Type (e.g., lined or unlined)

 Contact Medium (e.g., groundwater)

 Exposure Route (e.g., dermal)

 Receptor (e.g., child).
For each of the modeling scenarios, we devel oped a distribution of th eoretical risks at a unitized mass loading rate of 1 kg solvent in disposed wipes or laundry sludges per year managed in any landfill within the study population. Each distributi on represents a “scalable” set of risk estimates so that we can derive a mass loading limit for any percentile by (1) dividing the risk estimate into the risk level established by EPA and (2) multiplying that scaling factor by the unitized mass loading rate to derive a risk-based mass loading limit. Analysis Phase. In this phase, we implemented the risk analysis strategy by developing a modeling approach to estimate the hypothetical risks to adults and ch ildren due to (1) the inhalation of ambient air and (2) the use of groundwater as a drinking water source and for bathing/showering. For ambient air inhalation, the receptor locations were randomly sampled from 8 distances from the center of the landfil l (from 25 m to 2,000 m) al ong one of 36 radii at 10-degree increments. For groundwater exposures , the receptor well locations were located within the lateral exte nt of the groundwater plume with th e direction and distance randomly sampled from the edge of the municipal la ndfill up to 1,600 m (in the Analysis Section, see pages 3-30 and 3-36 for explanations of how r eceptor distances were selected for air and groundwater modeling, respectively). The risk modeling framework consists of a multiple models that are executed in series to estimate the distribution of risk. In essence, the modeling process can be summarized as follows:

1. The landfill source model estimates releases to the air and leachate at a fixed solvent concentration (1 kg solvent / m 3 waste) for lined and unlined landfills. For each solvent, the landfill simulation produces 10,000 mass loadi ng rates as function of the solvent concentration and landfill characteristics. 2. The source model outputs are norm alized to a unitized mass load ing rate of 1 kg per year by multiplying the result for each realization by a no rmalization factor. The normalization factor is simply the unitized rate (1 kg/yr) divi ded by the actual mass loading rate for that realization. This produces 10,000 so urce model results at the unitized mass loading rate for varying solvent concentrations. 3. The environmental fate and transport models use the outputs from the landfill source model (based on the unitized mass loading rate) to es timate concentrations in groundwater and in ambient air for randomly selected receptor locations. 4. The exposure model uses the estimated concen trations in contact media to estimate the solvent levels to which recep tors are exposed for (1) am bient air inhalation, and (2)

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groundwater use as a drinking water source and for bathing/showering.
5. For each modeling scenario, the risk model estim ates the solvent-specific cancer risk and noncancer hazard, as appropriate, for children and adults. This produces a theoretical distribution of risk at th e unitized mass loading rate.
With the exception of the landfill source model (referred to as the landfill coupled reactor model, or LFCR), the models used in this risk an alysis have been extensively peer reviewed and used to support a number of OSWER risk analysis . For example, the transport of leachate from the landfill through the unsaturated and saturated zones was evaluated quantitatively using EPA’s Composite Model for Leachate Migrati on with Transformation Products (EPACMTP) (U.S. EPA, 2003a,c,d; 1997b). This model has b een reviewed numerous times—most recently by EPA’s Science Advisory Board in 2004 (EPA , 2004a)—and has been used to support risk assessments at OSWER for over a decade, includi ng the relatively recent listing determination on the dyes and pigments wastes which was the subject of internal and extern al peer review (RTI, 2003). In contrast, the LFCR model was developed specifically for the wipes analysis to estimate the solvent-specific air emissions and leaching releases needed by the fate and transport models. The LFCR has received several levels of review, including reviews by internal experts (i.e., by members of the project team), EPA scientis ts and engineers, and external peer reviewers (as with the rest of the modeling methodol ogy). Based on comments provided by the peer reviewers, the LFCR has been modified in severa l key aspects, including the approach used to estimate solvent releases due to diffusion for the unlined landfill scenario. We assembled a variety of data sources to bu ild the input data sets required to run the modeling framework. When available, we relie d on information collected and developed for other OSWER risk assessments that (1) have go ne through a formal peer review process, (2) have been evaluated by EPA’s Science Advisory Board (SAB), or (3) some combination of the above. The data development generally followe d major Agency guidance documents, including, for example, the Risk Assessment Guidance for Superfund Volume 3 Part A: Process for Conducting Probabilistic Risk Assessment (RAGS 3A) (U.S. EPA, 2001a) and the Guiding Principles for Monte Carlo Analysis (U.S. EPA, 1997g), as well as Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing w ith Variability and Uncertainty in Models and Inputs (Cullen and Frey, 1999). Sections 3
, 4, and 5 , supported by the appendices, provide significant details regarding the t echniques and assumptions that went into the data development. The input parameter values and distributions are pr esented in their entirety in the appendices. It is important to note that we ma de direct use of input data that have previously undergone extensive review without necessarily subjecting th e data to further revision. For example, the data distributions for exposure factors were de veloped as part of the Multimedia, Multipathway, Multireceptor Risk Assessment (3MRA) modeling system, which was extensively reviewed by independent experts and EP A’s SAB (e.g., U.S. EPA, 2004a). However, because the probabilistic modeling was intended to capture the variability in environmental conditions across the contiguous United States, sp ecial care had to be taken to ensure that the parameters describing the environmental setting were internally consistent. Therefore, this section provides a brief overview of how location-based informa tion—specifically relate d to the hydrogeological and meteorological data—was addressed in set ting up the input database. To support the LFCR model, EPA developed a new database that repr esents the substantial variability in landfill geometry (e.g., surface area), and operating conditi ons (e.g., operating life). EPA used a variety of data sources to characterize landfills from the open scientific literature to expert judgment.

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All of these databases were processed to genera te input data sets that describe each of the modeling scenarios run through the Monte Carlo simulations (e.g., so lvent, landfill type, contact medium, receptor type). Thus, each realization in the theoretical distribution of 10,000 risk outputs represents a unique set of input values that were randomly selected with respect to the landfill characteristics, location, hydrogeology, receptor location, exposure factors, etc. Correlations—particularly correla tions among environmental setting characteristics associated with the landfill location—were retained as part of the sampling routines.

Risk Characterization. The risk assessment was conducted to derive a theoretical risk distribution (for both cancer and noncancer endpoints) that could be scaled to establish solventspecific RB-MLLs at the risk criteria determined by EPA. The risk criteria, which include the selection of a risk per centile from the distribution, reflect EP A’s consideration of the uncertainty associated with the risk estimates develope d using probabilistic modeling techniques. As discussed in Section 4.2 , certain assumptions and policy decisions made in response to data/model limitations tend to produce mass loading ra tes that may err on the side of being health protective. Risk distributions were generated for individual e xposure pathways for children and for adults, as well as for combined exposures a ssociated with groundwater usage. The combined exposures included drinking wate r ingestion, inhalation of volat ile solvents during showering (adult only), and dermal contact during bathing/s howering. Risk estimates for the inhalation of ambient air were not combined with the groundwa ter pathway estimates because our analysis of the time to peak well concentra tion showed that these exposures occur at very different time scales. Selected percentiles for the theoretical risk distributions for each modeling scenario are presented in Appendix A , including both the individual and combined exposure pathways (i.e., assuming a unitized mass loading rate of 1 kg per year). The theoretical risk estimates for the unitized mass loading rate provided some useful in sights into the nature of potential risks that could be associated with solvent management in municipal landfills:

 As expected, the predicted risks for the unlined scenarios were always greater than those for the composite-liner scenarios.

 At the unitized mass loading rate of 1 kg per ye ar, no theoretically predicted risks at the 50th or 90th percentiles were above the risk criteria for cancer or noncancer endpoints for either the unlined or composite-lined scenarios.

 For the unlined scenarios, the gr oundwater pathways were always associated with the highest predicted risks at the 90t h percentile of the distri bution (i.e., the upper end).

 For the composite-liner scenarios, this trend for highest predicted ri sks was true for all solvents but methylene chloride and methyl et hyl ketone, which showed higher risks for the inhalation of ambient air pathway.

 For solvents evaluated for cancer risks, 2 the highest cancer risks were predicted for adult receptors; for noncancer-causing solvents, the hi ghest hazard was typically predicted for children.

 The ingestion route of exposure was the most si gnificant route as compar ed to inhalation or dermal contact; however, combined exposures s howed the highest risks for roughly one-third of the solvents.

2 These solvents were: benzene (known human carcinogen under U.S. EPA, 1986), methylene chloride (probable human carcinogen), tetrachloroethylene (not classified in IRIS), 1,1,2-trichloroethane (possible human carcinogen), and trichloroethylene (cancer classifi cation withdrawn and under review).

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 For the unlined scenarios, the predicted risks at the 50th percentile showed similar trends as the predicted risks at the 90th percentile (e.g., gr oundwater-driven).

 For the composite-liner scenario s, the highest predicted risks at the 50th percentile were based on the inhalation of ambient air pathway for 16 solvents.
In addition to presenting the theoretical risk distributions, we describe the strengths and limitations of the risk analysis methodology, and address several critical comments offered by the external peer reviewers. 3 Limitations and uncertainties were primarily addressed through qualitative narrative that describes potential bias associated with certain model simplifications, data gaps, etc, with respect to the risk estimat es (i.e., is risk overestimated?). We developed several quantitative analyses to determine the significance of peer-revie w comments and explore the models’ behavior. Ultimately, the risk characte rization is intended to provide risk managers with information pertinent to the use and interp retation of the theoreti cal risk distributions. Below, we highlight the most important limitations , uncertainties, and analys es considered in the risk characterization.

 Receptor Wells. For each realization, it is implicitly assumed that a potable well is located somewhere between the edge of the landfill out to 1,600 m. In addition, the groundwater exposure pathways are modeled only for hypothetical individuals th at rely on receptor wells within the lateral exte nt of the plume. With respect to the general population, these are conservative assumptions that would tend to over-predict the groundw ater pathway risk.

 Landfill Locations. The geographic distribution of landf ills was based on a sample of 66 offsite Industrial D landfill locations, implying th at this population is representative of the population of MSWLFs in the contiguous United St ates. The veracity of this assumption is unknown.

 Receptor Locations. The receptor locations differed between the ambient air and groundwater exposure pathways. Therefore, we conducted an analysis to determine whether limiting the air receptors to the “lateral extent of the air plume” would result in a significant change in the risk estimates for ambient air. Based on that analysis, we found that the risk estimates would increase by less than a factor of 2 if we modeled only hypothetical receptors in the air plume (which would decrea se the RB-MLL by the same factor).

 Monte Carlo Simulation. One peer reviewer suggested that 10,000 iterations may be insufficient to characterize the theoretical risk distribution. We performed an analysis of convergence at 4,000, 6,000, and 10,000 iterations and demonstrated the stability of the 90th percentile in the distri bution of 10,000 iterations.

 Ecological Risk. The potential impacts on aquatic life via the groundwater-to-surface water pathway were not modeled. Based on availabl e ecological benchmarks, it appears that solvent levels protec tive of human health will also be protective of aquatic life.

 Leachate Handling. The LFCR does not simulate a leacha te collection system. In response to the peer review, we investig ated this uncertainty and determ ined that, because the LFCR is mass conserving, the inclusion of leachate colle ction would not significantly change the risk estimates for the inhalati on of ambient air pathway.

3 The entire methodology was externally peer reviewed, and a separate comment-response docum ent has been placed in the

docket, EPA-HQ_RCRA-2003-0004.

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 Liner Leakage Rates. The liner leakage rates are based on a relatively small empirical data set that represents some subset of liner beha vior (Tetra Tech, 2001). Thus, the use of these leakage rates is associated w ith an unquantifiable, and perhaps a significant level of uncertainty.

 Cover/Liner Characteristics. The cover/liner characteristics have a significant influence on the calculation of diffusion. The range of geom embrane permeation coefficients is based on empirical studies and, although there is no known bias in using the empirical studies, the degree to which these data are relevant to th e distribution of MSWLFs across the country is unknown.

 Landfill Characteristics. The area, height, and operating life of landfills were derived using summary statistics for each parameter from a commercial database (Waste Business Journal) obtained by EPA. Because the st atistical descriptors were not correlated, randomly selecting parameter values as independent variables resu lted in unrealistic landfills (e.g., 1 cm depth). Therefore, we derived a “representative” set of 10,000 landfills that (1) reflected reasonable engineering designs/practices (e.g., height), and (2) were generally consistent with the original summary statistics. The degree to which this derived data set represents the actual population of MSWLFs is unknown. However, co mparisons with other data sources on landfill size and operations indicate that this data set is an appr opriate representation.

 Dermal Contact. In response to peer review comments, dermal exposure to solvents during bathing/showering was modeled. EPA has identified the skin permeability coefficient required to calculate dermal dose as a paramete r with a high level of uncertainty. Given the fact that drinking water ingestion drives th e risk estimates, we do not consider this uncertainty to be significant for most solvents.

 Chemical Properties. Point estimates were used for all chemical properties, including biodegradation rates. EPA notes that the pauc ity of data on biodegrad ation introduces a high level of uncertainty in modeling this process. Because we sele cted conservative values (i.e., lowest biodegradation rate), we believe that the risks are likely to be over-predicted for some solvents.
Health Benchmarks. Point estimates of human health benchmarks were identified in EPAapproved sources. Although these benchmarks are derived to account for differences in intraspecies sensitivity and address the quality and quantity of supporting data, the uncertainty associated with their use has not been quantified. However, the primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the abse nce of scientific data to the contrary, should be health protective. Therefore, health benchmarks ensure that risks are not underestimated and therefore may lean toward s overestimating risks.



### Risk-Based Mass Loading Limits The risk-based mass loading rates are calculate d based on the 50th and 90 th percentiles from the theoretical risk distribution. For each modeling scenario (which includes individual exposure pathways and combined exposure path ways), the post-processing program

1. Selects the risk estimates at the 50th a nd 90th percentiles from the distribution 2. Calculates the ratio (referred to as the scal ing factor) between the risk estimate and the appropriate risk or hazard level

Do not cite or quote. ES-7Review Draft Executive Summary

3. Calculates the risk-based mass loading rate fo r each scenario by multiplying the unitized rate of 1 kg per year by the solven t-specific scaling factor 4. Checks the risk-based mass loading rate based on the solubility limit (if the solubility limit is exceeded, a solubility-based mass loading rate is calculated)
5. Selects the lowest mass loading rate from among all of the modeling scenarios at the 50th and 90th percentiles, respectively, and sets this value as the RB-MLL. The “scalability” of the unitized mass loading rate to a mass loading rate at a specific risk/hazard level is possible because the system is linear with respect to concentration and mass. Thus, the solvent-specific RB-MLL that corresponds to the 90th percentile of the theoretical risk distribution represents the “proba bility” (a level of confidence or “certainty”) that a randomly selected individual from the population of all individuals living around all landfills will be protected for the exposure pathways considered in this risk analysis. In this context, the term protective refers specifically to EPA’s risk crit eria for cancer risk and noncancer hazard. The cancer risk criterion used in this analysis is 10 -4 to 10 -6 ,. The lifetime cancer risk range of 10 -4 to 10 -6 is well established as a range fo r risk management purposes (40 CFR 300.430(e)(2)(i)(A)(2)).

Table ES-1. RB-MLLs Based on an HQ of 1 for 15 Solvents Analyzed Only as Noncarcinogens (kg/year)

No Liner Composite

Solvent 50%ile a 90%ile a 50%ile a 90%ile a
1,2- Dichlorobenzene 4.2E+04 N 7.1E+02 N 4.0E+06 S 1.4E+06 N

2-Ethoxyethanol 2.1E+06 N 2.3E+04 N 1.4E+09 S 8.1E+07 N

Acetone 8.4E+05 N 2.8E+04 N 1.1E+08 S 1.1E+08 S

Butanol 5.9E+05 N 1.2E+04 N 4.9E+08 S 1.2E+07 N

Chlorobenzene 3.1E+03 N 5.8E+01 N 1.4E+06 N 2.6E+04 N Cresols (total) 9.9E+ 04 N 1.4E+03 N 7.6E+07 S 1.4E+06 N Cyclohexanone 3.7E+06 N 9.9E+04 N 7.5E+06 S 7.5E+06 S Ethyl acetate 4.4E+08 S 5.3E+05 N 4.4E+08 S 4.4E+08 S Ethyl benzene 1.2E+05 N 2.1E+03 N 8.0E+06 S 2.6E+06 N

Isobutanol 2.4E+06 N 5.0E+04 N 2.5E+07 S 2.5E+07 S

Methanol 2.7E+05 N 4.9E+03 N 6.9E+08 S 1.2E+07 N Methyl ethyl ketone 8.0E+05 N 1.9E+04 N 3.0E+07 S 3.0E+07 S

Methyl isobutyl ketone 3.0E+05 N 6.2E+03 N 1.4E+07 S 1.4E+07 S

Toluene 4.9E+04 N 1.1E+03 N 1.3E+07 S 6.1E+05 N Xylene (mixed isomers) 2.3E+04 N 4.8E+02 N 8.5E+06 N 5.4E+05 N

a N = based on noncancer risk, S = based on solubility limit.

Do not cite or quote. ES-8Review Draft Executive Summary

Table ES-2. RB-MLLs Based on a Cancer Risk Level of 10 -4 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year)

No Liner Composite

Solvent 50%ile a 90%ile a 50%ile a 90%ile a
1,1,2-Trichloroethane 4.8E+03 N 1.1E+02 C 3.8E+06 C 1.0E+05 N

Benzene 1.0E+03 N 2.2E+01 N 4.9E+05 N 1.4E+04 N Methylene chloride 3.4E+04 N 8.0E+02 N 6.4E+06 N 9.7E+05 N Tetrachloroethylene (high) b 9.5E+02 C 1.7E+01 C 1.7E+06 C 8.2E+03 C Tetrachloroethylene (low) b 1.1E+03 C 2.0E+01 C 1.5E+07 C 9.8E+03 C Trichloroethylene 1.3E+04 C 2.2E+02 C 5.1E+06 C 1.3E+05 C

a C = based on cancer risk, N = based on noncancer risk.

b Two estimates are provided for tetrachloroethylene cancer risks to reflect the high

and low ends of the range of benchmarks.

Table ES-3. RB-MLLs Based on a Cancer Risk Level of 10 -5 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year)

No Liner Composite

Solvent 50%ile a 90%ile a 50%ile a 90%ile a
1,1,2-Trichloroethane 5.9E+02 C 1.1E+01 C 3.8E+05 C 1.1E+04 C Benzene (high) b 2.9E+02 C 5.0E+00 C 1.4E+05 C 3.6E+03 C Benzene (low) b 1.0E+03 N 1.9E+01 C 4.8E+05 C 1.3E+04 C Methylene chloride 5.4E+03 C 1.1E+02 C 2.2E+06 C 2.4E+05 C Tetrachloroethylene (high) b 9.5E+01 C 1.7E+00 C 1.7E+05 C 8.2E+02 C Tetrachloroethylene (low) b 1.1E+02 C 2.0E+00 C 1.5E+06 C 9.8E+02 C Trichloroethylene 1.3E+03 C 2.2E+01 C 5.1E+05 C 1.3E+04 C

a C = based on cancer risk, N = based on noncancer risk.

b Two estimates are provided for benzene and tetrachloroethylene cancer risks to

reflect the high and low ends of the range of benchmarks.

Table ES-4. RB-MLLs Based on a Cancer Risk Level of 10 -6 or an HQ of 1 for 5 Solvents Analyzed Both as Carcinogens and Noncarcinogens (kg/year)

No Liner Composite

Solvent 50%ile a 90%ile a 50%ile a 90%ile a
1,1,2-Trichloroethane 5.9E+01 C 1.1E+00 C 3.8E+04 C 1.1E+03 C Benzene (high) b 2.9E+01 C 5.0E-01 C 1.4E+04 C 3.6E+02 C Benzene (low) b 1.1E+02 C 1.9E+00 C 4.8E+04 C 1.3E+03 C Methylene chloride 5.4E+02 C 1.1E+01 C 2.2E+05 C 2.4E+04 C Tetrachloroethylene (high) b 9.5E+00 C 1.7E-01 C 1.7E+04 C 8.2E+01 C Tetrachloroethylene (low) b 1.1E+01 C 2.0E-01 C 1.5E+05 C 9.8E+01 C Trichloroethylene 1.3E+02 C 2.2E+00 C 5.1E+04 C 1.3E+03 C

a C = based on cancer risk

b Two estimates are provided for benzene and tetrachloroethylene cancer risks to

reflect the high and low ends of the range of benchmarks.

Do not cite or quote. ES-9Review Draft Section 1.0 Introduction



## 1.0 Introduction

1.1 Background
Approximately two billion wipes are used annually to clean industrial equipment contaminated with hazardous solvents. These so lvent-contaminated wipes include launderable shop towels and rags, paper to wels, and disposable wipes (collectively called disposed wipes) that are used by various industry sectors, such as publishing, prin ting, and automobile manufacturing and maintenance. The waste from th e use of wipes at facilities across the United States is managed according to existing state and federal regulations. Many states currently exclude laundered wipes from the definition of solid and/or hazardous waste, while disposed wipes are regulated as hazardous waste. F001–F005 Listed Solvents 1,1,2-Trichloroethane 1,2-Dichlorobenzene 2-Ethoxyethanol Acetone
Benzene Butanol Chlorobenzene Cresols (total) Cyclohexanone Ethyl acetate Ethyl benzene Isobutanol Methanol Methyl ethyl ketone Methyl isobutyl ketone Methylene chloride Tetrachloroethylene To support a proposed change in the regulatory status of disposed solvent-contaminated wipes and laundry sludges, EPA developed a risk analysis methodol ogy to characterize the potential risks associated with the dispos al of wipes and laundry sludge in nonhazardous waste landfills. The analysis estimated the amounts of the F001–F005 listed solvents (regulated by the Resource Conservation Recovery Act [RCR A], 40 CFR 261.31) shown in the box at right that, if c ontained on disposed wipe s or in laundry sludge, would not exceed EPA’s risk criteria if these materials were disposed of in nonhazardous waste landfills . This report describes the methodology developed to estimate the “allowable” amounts of solvents that could be disposed of in nonhazardous waste landfills. 4 Toluene Trichloroethylene Xylene (mixed isomers)

The risk analysis methodology has undergone an independent scientif ic peer review by experts outside EPA. The peer re view focused on technical aspect s of the analysis, including the construction and implementation of the Monte Carlo analysis, the selection of models to estimate the release of solvents from municipal landfills, th e subsequent fate and tr ansport of the solvents in the environment, and the characterization of ri sks resulting from potenti al exposures to human receptors. This report includes changes to the me thodology that were made in response to those comments, and represents a significant effort to improve the overall clarity and transparency of the methods, data, and models used to calculate the allowable mass loading rates. A copy of the peer reviewers’ comments and the Agency’s re sponses to these comments can be found in the docket (EPA-HQ-RCRA-2003-0004), which is located at www.regulations.gov.


### 1.2 Purpose
This risk analysis was conducted to estimate the amounts of specific F- listed solvents that can be disposed of in nonhazardous waste landfills without exceeding EPA’s risk criteria at the point of exposure. For this analysis, the term “nonhazardous waste landfills” refers specifically to municipal solid waste landfill s (MSWLFs), which were identified as the disposal destination for the solvent-contaminated wipes and laundr y sludge. The amounts are expressed as “mass

4 In this context, the term “allowable” refers to the maximum amount of solvent that can be landfilled each year and still meet EPA’s risk criteria.

Do not cite or quote. 1-1Review Draft Section 1.0 Introduction
loading rates” (in units of mass added to the land fill per year) and are intended to be protective of public health. Based on the results of the risk analysis, mass loading rates were derived for modeling scenarios defined in terms of the solvent, landfill type (e.g., lined or unlined), exposure pathway (e.g., ambient air i nhalation), contact media (e.g., groundwater), and receptor (e.g., child or adult). Specifically, the po ints of comparison used in this risk analysis stipulate that the allowable solvent mass from disposed wipes a nd laundry sludges managed in MSWLFs in the contiguous United States must

 Ensure that 50 or 90 percent of the hypothe tical individuals livi ng near a landfill will not be exposed to solvent releases at levels of concern. The level of concern depends on the type of effect:

 For carcinogenic (cancer-causing) solvents, the level of concern is defined as an excess lifetime cancer risk to indivi duals of 1 chance in 10,000 to 1 chance in 1,000,000 (i.e., 1 x 10 -4 to 1 x 10 -6 ) 5

 For solvents that cause noncancer health effect s, the level of concern is defined as a ratio of predicted intake levels to safe intake levels—the hazard quotient (HQ)—above 1. Note that an HQ less than or equal to 1 would be considered to be below a level of concern. Thus, the “problem statement” for this analys is may be defined as follows: the risk-based mass loading limit (i.e., the amount of mass allowe d to be managed in any MSWLF) should be protective of X percent (i.e., 90) of the hypothe tically exposed individu als across all of the landfill sites in the United States, with the term “p rotective” defined in terms of Y excess risk of cancer (i.e., 10 -5 ) and Z hazard (i.e., ≤ 1) for noncancer endpoints. Fo r the ambient air inhalation pathway, the exposed individuals are defined as in dividuals that reside within 2 kilometers (1.2 miles) of the edge of the la ndfill; for the groundwater pathwa y, the exposed individuals are assumed to use drinking water wells that are loca ted within 1 mile (~1.6 km ) of the edge of the landfill, within the lateral exte nt of the groundwater plume.


### 1.3 Methodology Overview
Mass loading rates were calculated for four exposure pa thways identified based on the solubility and volatility of the solvents being considered in the wipes analysis:

 At residential dwellings, inhalation of ambient air containing solvents emitted from the landfill

 Ingestion of solvents that leach from th e landfill and migrate through groundwater to residential drinking water wells

 Inhalation of solvents during shower ing with contaminated groundwater

 Dermal exposure to solvents during showering or bathing wi th contaminated groundwater (showering for adults and bathing for children).

Although other exposure pathways are known to exist (e.g., vapor in trusion of volatile gases into buildings), we focused the analysis on these exposure pathways to calculate the maximum amount of solvent mass that can be landf illed each year and meet the risk criteria. The allowable mass loading rates are identified by choo sing the lowest numerical value of any of the

5 The individual excess lifetime cancer risk range considered acceptable by EPA’s Office of Solid Waste and Emergency Response (OSWER) is 10 -4 to 10 -6 . In hazardous waste listings, the point of departure for listing a waste is 10 -5

.

Do not cite or quote. 1-2Review Draft Section 1.0 Introduction
pathway-specific mass loading rates. 6 We believe that this value will be protective for all potential exposure pathways associated with management of disposed wipes and laundry sludges for human and ecological receptors. To address the uncertainty inherent in modeling these exposure scenarios, a Monte Carlo modeling framework was implemented to produce a distribution of risk and hazard estimates with which to calculate a protective mass loading rate for each individual solvent. A probabilistic approach is particularly appropriate for the development of risk-based mass loading rates because solvent-contaminated wipes could be dis posed of in landfills of varying geometry and located in many different parts of the country. The probabilistic approach, as implemented, does not separate sources of uncertain ty from sources of variability la rgely because of the limitations in available data needed to parameterize the model. Thus, the approach primarily addresses variability in waste management practices, environmental settings (e.g., hydrology), and exposure-related parameters, su ch as distance from the landf ill and inhalation rates. From an overview perspective, the developm ent of allowable mass loading rates can be described by the following steps:

1. Collect data for all model inputs, includi ng the landfill model (e .g., landfill area), air dispersion model (e.g., wind speed ), groundwater model (e.g., depth to aquifer), exposure model (e.g., drinking water ingestion), and c onstituent-specific properties (e.g., cancer benchmarks, solubility constant).

2. Process the data and, as appropr iate, develop data distributio ns for all parameters that were assumed to vary (e.g., landfill geometry) and fixed values for all parameters that were not varied (e.g., health benchmarks).

3. Run the landfill model at a constant solvent concentration to estimate the release of solvents into the air and as leachate.

4. Normalize the results from the landfill model to a unitized mass loading rate, creating a distribution of mass releases base d on a rate of 1 kg per year.

5. Using the normalized concentrations of leachate from the landfill model, run the groundwater model to estimate th e solvent concentrations at the receptor well (referred to as exposure point concentrations).

6. Using the normalized solvent air emissions fr om the landfill model, run the air dispersion model to estimate the ambient air concentra tions of solvents at selected distances (referred to as exposure point concentrations).

7. Using the exposure point concentrations, run the exposure models to estimate the exposure distributions for each solvent, including exposure to vapor-phase solvents during showering and dermal expos ure to solvents during bathing.

8. Using the estimated exposures, run the risk mode l to generate a distribution of cancer risk and noncancer hazard for all scenarios included in the analysis.

9. Sort the cancer/noncancer results to identify pe rcentiles of interest from the distribution (e.g., 50th and 90th percentiles).

6 Note that for carcinogens, the groundwater pathways are added t ogether during the risk estimatio n. Therefore, the mass loading rate for combined exposures to carcinogens would be lower th an the pathway-specific mass loading rates for the individual groundwater pathways.

Do not cite or quote. 1-3Review Draft Section 1.0 Introduction

10. Calculate solvent-specific scaling factors for the percentiles of interest based on the ratio of the risk level specified by the risk criteria to the predicted risk.

11. For each solvent, multiply the unitized mass lo ading rate by the scaling factor for the selected percentiles of risk to derive the Risk-Based Ma ss Loading Limit (RB-MLL), the highest allowable solvent mass associated w ith disposed wipes and laundry sludges that can be managed in any given landfi ll and meet EPA’s risk criteria.


### 1.4 Document Organization
The remainder of this background document is organized into the following sections:

 Section 2.0, Problem Formulation , presents the conceptual a pproach for this analysis, including a description of th e modeling scenarios, the Monte Carlo framework, and the models used to estimate allowable mass loading rates.

 Section 3.0, Analysis , describes the development of the new landfill source model, the fate and transport modeling for air and gr oundwater pathways, the exposure model, and the risk calculations.

 Section 4.0, Risk Characterization , presents the key results from the Monte Carlo simulation and discusses the key uncertainti es and limitations of the risk analysis methodology.

 Section 5.0, Mass Loading Rates , describes how the results from the risk analysis were used to calculate the mass lo ading rates, and how the allo wable mass loading rates were identified. The appendices provide supplemental tech nical information and supporting data:

 Appendix A – Estimated Risks

 Appendix B – Chemical-Specific Parameters for Landfill Source and Shower Models

 Appendix C – Landfill Source Model Mathematical Formulation

 Appendix D – Landfill Source Model Testing

 Appendix E – Source Modeling Parameters

 Appendix F – Groundwater Modeling Parameters

 Appendix G – Unitized Air Concentrations

 Appendix H – Exposure and Risk Equations

 Appendix I – Human Exposure Factors.

Do not cite or quote. 1-4Review Draft Section 2.0 Problem Formulation



## 2.0 Problem Formulation
The risk analysis conducted for disposed solvent-containing wipe s and laundry sludges was designed to support the determination for appropriately managing solvent-contaminated wipes and laundry sludges in municipal solid wa ste landfills. The analysis estimated the amounts of spent solvents in disposed wipes and laundr y sludges that could be disposed in municipal nonhazardous waste landfills without exceeding the ri sk criteria adopted for this analysis (a 10 -4
to 10 -6 individual excess lifetime risk for cancer eff ects or a hazard quotient [HQ] of one for noncancer effects at the 50th and 90t h percentile of the proba bility distribution of risk or hazard). EPA defines the 90th percentile risk comparison for the risk-based mass loading limit to be protective of 90 percent of hypothe tically exposed individuals acro ss all of the la ndfill sites in the United States ( Guidance for Risk Characterization , U.S. Environmental Protection Agency, 1995; accessible at http://www.epa.gov/OSA/spc/ pdfs/rcguide.pdf, which states that “For the Agency's purposes, high end risk descriptors are plausible estima tes of the individual risk for those persons at the upper end of the risk distribution,” or co nceptually, individuals with “exposure above about the 90th percentile of the population distribution”). We also provide 50th percentile results as the central tendency estimate of that risk distribution. Specifically, we generated a theoretical 7 distribution of risk for the pathways most likely to lead to the highest exposure based on an initial solvent concentration of 1 kg solvent/m 3 waste. The risk-based mass loading rates were calculated at selected risk percen tiles (50th and 90th) for each solvent and modeling scenario defined in term s of the liner type, exposure route, receptor, etc. For each solvent and liner type, the 50t h and 90th percentile mass loading rates were compared across all of the modeling scenarios, an d the lowest value identified. This risk-based mass loading rate (in kg solvent/year) is the maximum solvent mass that can be added to a landfill in any given year and be considered protective of individuals living near landfills used for wipes disposal for the ev aluated exposure pathways. Defining the objective of the risk analysis as supporting the determination of allowable mass loading rates has significant implications w ith respect to how the risk analysis was constructed. For example, we did not model the in cidental ingestion of contaminated water for recreational swimmers, although it is possible that a groundwater-t o-surface-water breakthrough could result in a non-zero exposure in certain communities in which municipal landfills are located. This scenario was excluded from the ri sk analysis largely be cause there are several recent landfill scenario risk anal yses indicating that this pathway is not a significant contributor to the overall risks, and therefore, including th is pathway would not change the risk-based mass loading rate (e.g., see the draft ri sk analysis for Coal Combusti on Waste [RTI, 2007] and the risk analysis for the Dye and Pigment Waste Lis ting [U.S. EPA, 2003b]). During the problem formulation stage, EPA determined that only the most direct exposur e pathways should be evaluated in developing the risk-based mass loadi ng rates. Similarly, given the resource and time constraints, the risk analysis excluded potential impacts on aquatic life due to the groundwaterto-surface-water pathway because (1) this woul d have required site-specific information on nearby water bodies to determine whether or not ther e were, in fact, valued aquatic attributes that should be protected, and (2) based on the physi cal and chemical properties of the solvents

7 Theoretical in the sense that the results were based on an a ssumed concentration rather than on solvent concentrations from analytical sampling.

Do not cite or quote. 2-1Review Draft Section 2.0 Problem Formulation
evaluated, we expect that natural diluti on and attenuation woul d reduce the solvent concentrations below levels of ecologi cal concern in receiving water bodies. 8
Given the objective of the wipes’ risk analys is, the purpose of this problem formulation is to provide an understanding of the methodology deve loped to derive the ri sk scaling factors and explain how these factors were used to calculate the risk-based mass loadi ng rates. The details of each of the models used in the risk analysis —as well as the data developed to support those models—are presented in Section 3 and the appendices. In this section, we present the two critical outcomes of the problem formulation:

 The conceptual model illustrates the relationshi ps among the landfill source, environmental compartments, and human receptors, and describes the modeling scenarios that were evaluated in this risk analysis ( Section 2.1 ).

 The analysis plan presents the overall modeling approach developed for this risk analysis and summarizes how the model was impleme nted; that is, it describes the modeling framework and the flow of information th rough models that simulate the release, transport, exposure, risk, and allowable mass loading rates ( Section 2.2 ).


### 2.1 Conceptual Model
The conceptual model depicted in Figure 2-1 is a simplified re presentation of how solvents in disposed wipes and laundry sludge move in the environment, from their release from municipal solid waste landfills (MSWLFs), transport through various environmental compartments, and contact with environmen tal media (e.g., groundwater) by human receptors. After release from an MSWLF, solvents can be dispersed in ambient air or migrate from the leachate into the underlying aquifer. Thus, resi dents living near an MSWLF could be exposed through the inhalation of contaminated air, inge stion of contaminated groundwater as drinking water, inhalation of volatile so lvents or dermal contact duri ng showering and bathing with contaminated groundwater.

Figure 2-1. Conceptual model for modeling solvents in wipes.

8 Section 4, Risk Characterization, explores these assumptions and addresses several comments pr ovided by peer reviewers.

Do not cite or quote. 2-2Review Draft Section 2.0 Problem Formulation
In developing the conceptual model, we c onsidered the physical and chemical properties of the solvent constituents of concern, as well as the operating practices of MSWLFs that are relevant to environmental releases of organic solvents. As shown in Appendix B , the 20 solvents evaluated include a wide range of volatile and se mi-volatile organic chemicals, most of which have relatively short environmenta l half-lives (as compared to pe rsistent organic chemicals). As with most organic solvents, diffusion and adv ection into the air are the primary release mechanisms into the environment and are the ma in reason many of these solvents can be used only with adequate ventilation. Moreover, a number of these solvents (e.g., benzene, toluene, and xylene) have been identified as groundwat er contaminants at Superfund and Resource Conservation and Recovery Act (RCRA) Correctiv e Action sites. Conseque ntly, the leaching-togroundwater pathway was also considered a pr imary release mechanism. The generally low values for partition coefficients for these solven ts strongly suggests th at indirect exposure pathways (e.g., plant uptake of vapors and subseq uent ingestion of contaminated produce) will either be incomplete or contribute negligibly to total exposure. The conclusion that volatile and semi-volatile organic solvents are insignificant contributors to risk via indirect exposure pathways (for a landfill source) is consistent with other risk analyses of landfill waste management scenarios (see for example, the Pain ts listing risk analysis [U.S. EPA, 2001b]). Thus, direct exposure pathways to air and gr oundwater were the primary focus of the risk modeling for wipes and laundry sludge. The conceptual model is a particularly importa nt part of the problem formulation because it serves as the basis for a number of decisions critical to the design of a risk analysis. For example, the release mechanisms from the la ndfill are dominated by vapor diffusion, advection, and leaching. Because no landfill model was identified that simulated these processes simultaneously (at a scale and resolution appropria te for a national rulemaking), we developed a landfill model specifically for this risk analysis. In addition to informing the selection (or development) of appropriate models, the conceptual model is also used in identifying the specific scenarios for which the models will be run. These modeling scenarios must reflect the rela tionships shown in Figur e 2-1 and be relevant to the risk management goals—in this case, th e development of risk-based mass loading rates that are protective of public he alth. Thus, we defined the mode ling scenarios in terms of

 Characteristics of the waste (i.e., solvents managed in the MSWLFs)

 Characteristics of the landfill (i.e., liner type)

 Contact media (i.e., environmental medium to which humans could be exposed, such as air or groundwater)

 Exposure routes (i.e., how the solvent can be taken up by receptors, su ch as inhalation or ingestion)

 Receptors of concern (i.e., types of human recep tors that can be exposed, such as adults or children).

Whereas the conceptual model shows the proc esses and environmental compartments that are in the modeling domain, the modeling scenario de fines the attributes of a specific simulation that are relevant to the decisi on-making process. For example, the results of a simulation for
acetone released from an unlined landfill followed by the ingestion of contaminated groundwater by children represent a specific modeling scenario. Figure 2-2 shows attributes used to define modeling scenarios. For this risk analysis, we defined about 4 00 modeling scenarios that were

Do not cite or quote. 2-3Review Draft Section 2.0 Problem Formulation
considered in calculating allowable mass loadi ng rates based on potential health risks. Not all possible combinations of the attr ibutes shown in Figure 2-2 were run; for example, solvents lacking an inhalation benchmark were not run for inhalation pathways ; showering exposures were not considered for childre n; and only the inhalation exposu re route was modeled when the contact medium was air or shower vapor (and si milarly, only the ingestion and dermal exposure routes were modeled for groundwater).

Figure 2-2. Modeling scenarios ev aluated in developing risk-based mass loading rates.


### 2.2 Analysis Plan
The analysis plan is organi zed into four components. Section 2.2.1 presents a short discussion of the risk analysis strategy as it pertains to the dimensions of the modeling framework, including the spatial and temporal sc ales, the receptors of interest, and health endpoints. Section 2.2.2 describes the probabilistic modeling framework developed for this risk analysis, touching on both the theory and mechanics of the analysis. Section 2.2.3 summarizes how the model was implemented, briefly describing each of the major modeling components (e.g., landfill model, exposure model). Section 2.2.4 provides an overview of how data were developed to support the risk analysis modeling.

2.2.1 Risk Analysis Strategy

As discussed in Section 1 , the risk analysis strategy supports a national rulemaking for determining the appropriate management practic e for solvent-contaminated wipes and laundry sludge in MSWLFs. Consequently, it is important to recognize that the strategy relies on representing the most significant potential risks due to long-term, low-level solvent releases from MSWLFs; this will ensure that EPA can place a hi gh level of confidence on the protectiveness of the allowable mass loading rates. Implicit in this strategy are three key assumptions: (1) shortterm inhalation exposures during unloading and spr eading of waste at the landfill will be subject to occupational regulations and will therefore be protective for residents living offsite, (2) it will take tens or even hundreds of years for a solvent plume to travel from underneath the landfill to a nearby drinking water well, and (3) EPA’s human health benchmarks for chronic exposures are appropriate effects measures given our modeling scenarios. With these assumptions in mind, we can describe the dimensions of the modeling framework as follows:

Do not cite or quote. 2-4Review Draft Section 2.0 Problem Formulation

 Spatial scale. A representative analysis can be conducted usi ng a model with a very low level of spatial resolu tion (e.g., nationally averaged data inputs) or with a more refined model at a more precise spatial scale. 9 For this risk analysis, we chose to use models at a more refined scale (i.e., site -based) and aggregate the resu lts for hypothetical sites across the country into a national profile. The spatial boundaries for which exposures were assessed were 1.6 km (1 mile) from the edge of the landfill for groundwater wells and 2 km for ambient air.

 Temporal scale. Because we were concerned with long-term releases and resulting chronic exposures to solvents, the tempor al scale for the modeling framework was aggregated to provide annual average concen trations in air and groundwater to which receptors may be exposed. The landfill model developed for this analysis is capable of producing flux estimates at much shorter time steps; however, the temporal scale for the overall model chain (e.g., source model to gr oundwater model to exposure model) is based on annual average concentrations and flux rates.

 Exposures. Exposures to ambient air occur in a ve ry different timeframe than exposures to groundwater. Therefore, we did not aggr egate groundwater and am bient air exposures for any constituent of concern. However, for carcinogens, exposures to groundwater via drinking water ingestion were added to th e inhalation exposure and dermal exposures during showering, as appropriate and consistent with EPA guidance. For noncarcinogens, exposures to groundwater via these routes were also combined for solvents with inhalation and ingestion health endpoints indicating that the target organ and effect are independent of the exposure route.

 Receptors. Residential exposures for adults and children were considered. Although this is a site-based analysis, the risk estimates are individual risks, not population-based risks. That is, the distribution of ri sks represents risk estimates for hypothetical individuals for specific modeling scenarios rather than the ri sks to a population living near a landfill that manages solvent-containing wipes and laundr y sludges. For the groundwater pathways, the receptor wells were randomly located downgradient within the lateral extent of the plume up to a distance of 1 mile from the edge of the landfill. (See Section 3, page 3-36, for further details on groundwater receptor loca tions.) For the air pathways, the receptors were randomly placed along one of 36 radii (at 10-degree increments) and located up to 2 km (~1.2 miles) from the edge of the landf ill. (See Section 3, page 3-30, for further details on air receptor locations.)

 Health endpoints. For chronic exposures to solven ts released from MSWLFs, we evaluated two types of health endpoints for each constituent of concern: cancer effects and noncancer effects. As with exposure, cancer risks were aggregated for the groundwater pathway for each solvent. Cancer risks were not summed across all solvents that are carcinogens because the intent of this risk analysis was to support solventspecific mass loading rates. Noncancer hazard was aggregated only for those solvents with health endpoints relevant to the same ta rget organ and effect: benzene, cresols, and methyl ethyl ketone.

9 There are numerous ways to conduct a repr esentative assessment, which span the con tinuum of very course to very precise models. However, we have chosen to use two very basic appro aches to illustrate spatial scale for the wipes risk analysis.

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2.2.2 Risk Modeling Approach To address the substantial variability across landfills and locations for a national analysis, we adopted a probabilistic modeling approach co mmonly referred to as a Monte Carlo approach. In essence, a Monte Carlo simulation can be pe rformed when probability distributions can be developed for some or all of the input parameters. The probability distributions for each parameter are sampled to develop a data set of th ousands of individual model runs referred to as realizations. A Monte Carlo approach is particular ly appropriate for a risk analysis of landfills with variable characteristics th at are located across a wide ra nge of environmental conditions. The Monte Carlo simulation for this analysis captures much of the variability in parameters that describe the landfill (e.g., operating practices), environmental setting (e.g., meteorology, hydrogeology), and human exposure factors (e.g., receptor distance, drinking water ingestion rates). This approach also pr opagates uncertainty associated with parameter values. The uncertainty is addressed along with variability but is not describe d separately in this analysis. The Monte Carlo simulation produc es a distribution of exposur e and risk which, for this risk analysis, was based on an assigned initial concentration for each solvent of 1 kg solvent/m 3 waste. From this distribution, we can select any percentile from the distribution for the purposes of deriving a risk-based mass loading rate (explained in detail in Section 5 ). The distribution represents the variability in risk associated with location, landfill ch aracteristics, and other inputs such as drinking water ingestion rates. As implemented, the Monte Carlo simulation does not distinguish between uncertainty and variability, and because the ou tput distribution prim arily represents the variability in the input parameters, the Monte Carlo approach addresses what is ge nerally referred to as Type A uncertainty (the uncertainty associated with variability) (Hoffman and Hammonds, 1994). In essence, distributions were sel ected to represent vari ability (e.g., exposure factors) and in some cases, to also represent the uncer tainty in the true parameter value (e.g., depth to groundwater table). The uncertainty around the true mean (or ot her percentile risk) a nd variance of the risk distribution are not addressed in th e sense that the statistical descriptors for each input parameter remain constant throughout the simulation, and the model output is a single distribution of risk for each solvent. Type B uncertainty—the uncerta inty associated with a lack of knowledge—was not addressed separately from variability. Type B uncertainty is distinguished from Type A uncertainty by conducting the Monte Carlo simulati on in two dimensions; this makes it possible to estimate a confidence interval around the pr obability density functi on (PDF) and therefore allows the uncertainty in the overall result to be quantified. For national analyses such as this one, it is difficult to separate variability from uncertainty (Nauta, 2000), and a two-dimensiona l probabilistic approach would have been impractical in terms of time and resources. For ex ample, the number of simulations that could realistically be performed with in the modeling chain for landfill -to-groundwater-to-air dispersion was prohibitive because of the added time dimension necessary when running the source, fate and transport, and exposure models. Although a two-dimensional Monte Carlo framework can provide additional insight into uncertainty by separating vari ability and uncertainty, a onedimensional probabilistic approach was used for the analysis that commingles variability and uncertainty into a single dimension (Mokhtari and Frey, 2005). As explained below, the risk distribution from the model simulation represents a best estimate of the dist ribution of risk for a unitized mass loading rate and varying solvent co ncentrations, accounting fo r multiple sources of uncertainty and variability, especially the va riability in the input parameter values.

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Figure 2-3 is a simple depiction of the Monte Carlo framework developed for the risk analysis. It shows how the distri butions for input parameters we re sampled and used to produce the PDF and cumulative distribution function (CDF ) from which specific percentiles (e.g., 90th percentile) can be identified. The parameters A, B, and C each have their own distributions, which may represent variability or uncertainty, or both. For each realization of the model, a single value was sampled from each input distribu tion regardless of the type of variation (i.e., variability or uncertainty). For each modeling sc enario, the simulation produced the PDF of risk results, as shown in Figure 2-3 (i.e., the distribu tion of risks across exposed individuals across all sites represented in the analysis ). Lastly, the CDF was created a nd the specific percentiles (e.g., 90th percentile) were select ed and used to derive the risk-based mass loadings.

Figure 2-3. How the Monte Carlo framework addresses uncertainty.

For the Monte Carlo simulation, the syst em was run 10,000 times for each modeling scenario, with each realization producing a sing le result. The site-based approach ensured consistency among related parameters for a give n realization in the Monte Carlo database, avoiding unrealistic parameter combinations. Th e simulation output for each modeling scenario was a distribution of 10,000 values—cancer ri sk or noncancer hazard—representing the distribution of possible outcomes given the underl ying variability (and uncer tainty) in the data used in the analysis.

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The mechanics of the Monte Carlo m odeling framework are depicted in Figure 2-4 . The figure shows that the landfill source model performed 10,000 ite rations for composite-lined landfills and 10,000 iterations for unlined landfills, with identical values for all inputs except those related to liner type for each realization (i.e., the only differe nce in the respective data sets was the leakage rates). The two sets of 10,000 iterations were then used as input to the air dispersion model and the groundwater model, and the simulation was carried through to produce a distribution of risk or hazard for each receptor. 10



### Modeling Scenario Solvent x Landfill Type x Contact Medium x Exposure Route x Receptor

Drinking

water Groundwater Landfill Source Model

No Liner Composite

Ambient Air

ISCST3 EPACMTP Solvent

10,000 iterations

Shower/Bath a

Inhalation Ingestion

Adult Child Inhalation

Adult Child 10,000 iterations

Adult Dermal

Adult Child

a Shower for adult, bath for child

Figure 2-4. Basic Monte Carlo structure.

10 In executing the Monte Carlo simulation, results were only pa ssed to the downstream models if a health benchmark was available. For example, if a solvent did not have an EPA-a pproved inhalation health benchmar k, the results from the landfill source model were not being passe d to the air dispersion model. Similarly, the groundwater pathway involving inhalation during showering was not run in those cases.

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2.2.3 Model Implementation As suggested in Figure 2-4, the modeling fram ework developed for this risk analysis consists of a series of modules that are executed in a specific orde r to estimate the distribution of risk. The modeling process can be summarized as follows:

 The landfill source model estimates solven t releases to the air and leachate

 The environmental fate and transport models estimate concentrati ons in groundwater and ambient air

 The exposure models estimate the solvent le vels to which receptors are exposed

 The risk model estimates the solvent-specific cancer risk and noncancer hazard, as appropriate for the unitized solvent lo ading rate of 1 kg solvent per year

 The post-processing program estimates mass loading rates after the modeling simulation is complete.

The major functionality of the models implem ented in this risk analysis is described briefly below.

Landfill Source Model. A landfill coupled reactor (LFCR) model was developed for this analysis to estimate the solvent-specific air emis sions and leachate fluxes needed by the fate and transport models. The LFCR model represents the landfill as a series of fully mixed reactors by discretizing the landfill into a series of daily cells. The LFCR model describes mass partitioning of solvents among the solid, water, and air pha ses through linear equilibrium relationships (Henry’s law and a solid-water distribution coefficient). Thus, the Monte Carlo simulation first ran the LFCR model at an initial fi xed solvent concen tration of 1 kg/m 3 . After the landfill modeling runs were complete, the results for ea ch solvent (i.e., 10,000 ite rations for lined and 10,000 iterations for unlined landfills) were normalized to a constant mass loading rate of 1 kg/yr (as mass loading rate is a function of solvent co ncentration and landfill characteristics). This set of results was then used as input for all downstr eam models, beginning with fate and transport. A detailed discussion of the LFCR model is presented in Section 3.1

. Fate and Transport Models. The Industrial Source Complex – Short Term Version 3 (ISCST3) air dispersion model was se lected to estimate the solvent concentrations in ambient air after release from MSWLFs. The unitized air concentrations ( UACs) estimated by ISCST3 are based on a unit emission rate of 1 μg/s-m 2 . The UACs were multiplied by the solvent-specific volatile emission rates predicted by the LFCR sour ce model to estimate a time series of annual average solvent concentrations in ambient air. These air concentrations were combined into a database of annual average concentrations for all modeled locations in the United States, 19 representative surface areas for MSWLFs, 7 discre te distances from landfill to receptor, and 36 directions (at 10 degree increments) relative to the landfill. A detailed discussion of the air modeling is presented in Section 3.2

. EPA’s Composite Model for Leachate Mi gration with Transformation Products (EPACMTP) groundwater model (U.S. EPA, 2003a ,c,d) was selected to estimate solvent concentrations at the receptor well using th e leachate estimates produced by the LFCR model. EPACMTP simulates steady-state flow in both th e unsaturated zone and the saturated zone beneath a waste disposal unit—in this case, an MSWLF—to estimate the concentration arriving downgradient at the well, which for this analysis was always placed in the contaminant plume. Modeling flow as steady state assumes that conditions (e.g., infiltration) are representative of

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long-term average conditions that do not change over the duration of the simulation. The solvent transport, however, is modeled as a time-depende nt finite source that ta kes into account the time period over which a landfill operates and releases c ontaminants, as well as the actual time it may take contaminants to reach the receptor well. The output of EPACMTP is a database of annual peak and time-averaged solvent concentrations arriving at randomly selected downgradient groundwater receptor well locati ons. A detailed discussion of the groundwater modeling is presented in Section 3.3

. Exposure Model. The exposure model has two primary functions:

 Calculating the lifetime average daily dos e (LADD) and the average daily dose (ADD) for the adult and child receptor due to dri nking contaminated groundwater from a nearby receptor well and the dermal absorbed dose (DAD) due to dermal contact during showering (adult) or bathing (child) in contaminated groundwater. The LADD is calculated for cancer endpoint s and the ADD is calculated for noncancer endpoints; each type of dose is a function of several expos ure factors, including the drinking water consumption rate and exposure duration. Th e DAD is calculated for both cancer and noncancer endpoints and is a function of severa l exposure factors, including skin surface area and event frequency. A de tailed discussion of the expos ure modeling is presented in Section 3.5

.

 Calculating the air concentration to which the adult receptor is exposed due to the inhalation of indoor air associated with showering. The shower model is a special component of the exposure model developed under previous landfill scenario risk analyses (e.g., U.S. EPA, 2003b). The shower model uses the groundwater concentrations predicted by EPACMTP to calculate a time-we ighted average air concentration for an entire day and estimates the annual average air exposure concentration. 11 A detailed discussion of the shower model is presented in Section 3.4

.

Table 2-1 shows the exposure model outputs passed to the risk model by receptor and route of exposure. Note that the solvent concen trations used in estimating risk due to the inhalation of ambient air are calcul ated by ISCST3 and passed directly to the risk model. That is, the exposure model is not used in calculating exposure concentrations for the ambient air pathway. However, for completeness, the ambient air pathway is shown in the table. Table 2-1. Exposure Model Outputs by Receptor and Exposure Route

Receptor Inhalation (Ambient Air) Ingestion (Drinking Water) Inhalation (Indoor Air—Shower) Dermal (Shower or bath a )

Adult resident Air concentration (from ISCST3) ADD (noncancer) LADD (cancer) Shower air concentration DAD (noncancer) DAD (cancer)

Child resident Air concentration (from ISCST3) ADD (noncancer) LADD (cancer) Not evaluated DAD (noncancer) DAD (cancer)

a Shower for adult, bath for child.

11 We considered only adult receptors in evaluating the shower scenario. As with previous EPA landfill scenario risk assessments, we assumed that children woul d tend to take a higher proportion of bath s. Exposure factors for teenagers are simil ar to adults; therefore, including a teenaged cohor t would add little value to the risk estimates.

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For the child receptor, we did not simulate volatilization from the bath and subsequent inhalation for two reasons. Firs t, for noncancer endpoints, the h azard is estimated by comparing the RfC to the air concentration. Because first principles show th at the solvent mass that enters the air during showering is much greater than the solvent mass that could enter the air during bathing, the use of the RfC will be protective of both adults and children. Second, for cancer endpoints, adult exposures have been shown to drive the risks. A re view of Equation 3-11 indicates why that is typically the case: in additi on to the solvent concentration in air, the risk estimate is based on the ratio between exposure duration and averaging time, which is almost always higher for adults than for children. The Risk Assessment Guidance for Superfund Volume 3 (RAGS) Part E (U.S. EPA, 2004b) states that even though children may be bathing for a longer duration, the showering adult remains the most highly exposed receptor. Therefore, the adult shower inhalation pathway will provide a sufficiently protective estimate of potential risk/hazard from inhalation while bathing for all receptors.

Risk Model. The outputs from the risk model are shown in Table 2-2 ; cancer risks are the excess risk to an exposed individual (e.g., 1E- 05 means an individual’s excess lifetime cancer risk of 1 chance in 100,000) and noncancer hazard is the ratio between exposure and a threshold that EPA has established that is likely to be w ithout an appreciable risk of adverse effects (this ratio is typically referred to as the hazard quotient, or HQ). This module uses human health benchmarks developed by EPA for the purposes of assessing chronic risks for noncancer and cancer endpoints associated w ith long-term exposures to ch emical constituents. For the inhalation of solvents with cancer benchmarks, the cancer risk was estimated by multiplying the appropriate benchmark (inhalati on Unit Risk Factor [URF]) by the exposure concentration. For the inhalation of solvents with noncancer be nchmarks, the hazard was estimated simply by calculating the ratio between the predicted e xposure concentration in air and the health benchmark (the Reference Concentr ation [RfC]). Note that this calculation does not distinguish between adult and child receptors. For the ingestion of solvents with cancer benchmarks, the risk was estimated by multiplying the LADD by the appropr iate benchmark (the Cancer Slope Factor [CSF]). For the ingestion of solvents with nonc ancer benchmarks, the hazard was estimated as the ratio of the ADD to the health benchmark (the Reference Dose [RfD]).

Table 2-2. Outputs of the Risk Model by Receptor and Pathway

Receptor Ambient Air Pathway Groundwater Pathway

Adult resident  Cancer Risk

 Noncancer Hazard  Cancer Risk – Ingestion

 Cancer Risk – Inhalation

 Cancer Risk – Dermal

 Combined Cancer Risk – Ingestion + Inhalation + Dermal

 Noncancer Hazard – Ingestion

 Noncancer Hazard – Inhalation

 Noncancer Hazard – Dermal

 Combined Noncancer Hazard – Ingestion + Inhalation + Dermal a

Child resident  Cancer Risk

 Noncancer Hazard  Cancer Risk – Ingestion

 Cancer Risk – Dermal

 Combined Cancer Risk – Ingestion + Dermal

 Noncancer Hazard – Ingestion

 Noncancer Hazard – Dermal

 Combined Noncancer Hazard – Ingestion + Dermal a

a Combined noncancer hazard was calculated only for solvents with inhalation and ingesti on health endpoints indicating that the target organ and effect are independent of the exposure route.

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For the groundwater pathways, we estimated risks for each individual exposure route for the adult (ingestion of drinking water, inha lation during showering, dermal contact during showering). Because these exposure routes can occur during the same time period for carcinogens, we also calculated a combined ri sk by summing the risks for these three exposure routes. For any given solvent, the summa tion required that (1) an inhalation and ingestion benchmark for carcinogenicity were available, and (2) study data did no t suggest a portal-ofentry effect. This approach may slightly overes timate combined risk because the model does not subtract the solvent mass available for one s hower exposure route (e .g., inhalation) from the mass available for the other shower exposure route (e.g., dermal contact). For children, we estimated the combined cancer risk due to ingest ion of drinking water an d dermal contact during bathing by summing the route-specific risk estimates.

The noncancer hazards were summed for the groundwater pathways only for solvents with inhalation and ingestion he alth endpoints indicating that th e target organ and effect are independent of the exposure route.

A detailed discussion of the risk model is presented in Section 3.6

. Mass Loading Rate Model. The model developed to calculate risk-based mass loading rates is relatively straightforward and relies on the fact that the risk modeling framework described above is linear with respect to both solvent concentration and mass loading rate. Given the linearity of the system, the simplest and most transparent way to calculate a risk-based mass loading rate is to derive a s caling factor. From the hypothetical distributions for cancer risk and noncancer hazard, we calculated the ratio of the es timated risk/hazard at specific percentiles to the risk/hazard criteria. We then used this rati o as a scaling factor to increase or decrease the unitized mass loading rate, resulting in a mass lo ading limit at that specific percentile of risk, referred to as the risk-based mass loading limit (RB-MLL). For example, for each modeling scenario, the RB-MLL at the 90th percentile risk is considered protectiv e of 90 percent of the hypothetical exposed individuals at the relevant risk criterion. For example, if the 90th percentile cancer risk for wipes contaminated with chlorobenzene was 5 per 100,000 (i.e., 5E-05), the scaling factor would be 0.2 (1E-05 / 5E-05). Mu ltiplying the unitized mass loading rate of 1 kg solvent/year by the scaling factor of 0.2 results in a risk-based mass loadi ng rate of 0.2 kg/year; this is the allowable mass loading rate that is presumed to be protec tive of 90 percent of the individuals exposed to chlor obenzene-containing wipes mana ged in municipal landfills.

2.2.4 Data Development To support the Monte Carlo simulation, we used a variety of data sources. In particular, we relied on information collected and develope d for risk analyses that (1) have gone through a formal peer review process, (2) have been ev aluated by EPA’s Science Advisory Board (SAB), or (3) some combination of the above. The probabilistic modeling approach and data development generally follow major Agency guidance documents, including, for example, the Risk Assessment Guidance for Superfund Volume 3 Part A: Process for Conducting Probabilistic Risk Assessment (RAGS 3A) (U.S. EPA, 2001a) and the Guiding Principles for Monte Carlo Analysis (U.S. EPA, 1997g), as well as Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs (Cullen and Frey, 1999). Sections 3
, 4, and 5 , supported by the appendices, provide significant details regarding the techniques and assumptions th at went into the data development. The input parameter values and distributions are presented in their entirety in the appendices. It is important to note that we

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made direct use of input data that have previously under gone extensive re view without necessarily subjecting the data to further revision. For exampl e, the data distributions for exposure factors were developed as part of the Multimedia, Multipathway, Multireceptor Risk Assessment (3MRA) modeling system, which was extensively reviewed by independent experts and EPA’s SAB (e.g., U.S. EPA, 2004a). Howe ver, because the probabilistic modeling was intended to capture the variability in envir onmental conditions acro ss the contiguous United States, special care had to be taken to ensure that the parameters describing the environmental setting were internally consistent. Therefore, this section provides a brief overview of how location-based information—specifically relate d to the hydrogeological and meteorological data—was addressed in sett ing up the input database. The development of the input data set to support a location-based Monte Carlo simulation began with the selection of MSWLF locations. We used an approach (and data) similar to that used in the peer reviewed risk analysis to de rive allowable mass loadi ng rates for the dye and pigment industry (U.S. EPA, 2003b). Lacking information on the spa tial distribution of MSWLFs, municipal landfill locations for this ri sk analysis were based on the locations of industrial landfills found in EPA’ s Industrial D database (U.S. EPA, 1987). As with the dyes analysis, we implicitly assumed that offsite Industrial D landfills provide a reasonable representation of the di stribution of MSWLFs across the Unite d States. The subset of 66 offsite landfills 12 was equally weighted and sampled 10,000 times to obtain a sample population of locations and correlated parameters (e.g., aquife r type, climate center, soil types, aquifer temperature) necessary to run the LF CR, EPACMTP, and ISCST3 models. The databases developed to support the EPAC MTP groundwater model were used as the primary source for these correlated environmenta l parameters. EPACMTP has been the subject of multiple peer reviews and has been through a formal review process with EPA’s SAB. In addition, the EPACMTP and supporting databases have been used in at least eight risk analyses that have been through a combination of peer and SAB reviews (e.g., the chlorinated aliphatics listing; the Surface Impoundment Study). Thus, we used these data with a high level of confidence that the methods used to collect and develop this information were consistent with Agency guidance and best practices with re spect to probabilistic risk analysis. Two major groups of input data used by the fate and transport models required careful correlation; these are described below. Hydrogeologic Environments. The LFCR and EPACMTP mode ls require a number of hydrogeologic parameters. Therefore, the input data set was developed using the correlated, national aquifer property data from the Amer ican Petroleum Institute (API) Hydrogeologic Database (HGDB) by associating each of th e landfill locations w ith a hydrogeologic environment (e.g., Newell et al., 1990). EPACMTP uses the HGDB data to specify probability distributions for each of the following hydrogeologic parameters:

 Unsaturated zone thickness

 Aquifer thickness

 Hydraulic gradient

 Longitudinal hydraulic conductivity.

12 The 790 sites in the landfill database were se gregated into onsite units (724 sites, a pproximately 92 percent of the sites) and offsite units (66 sites, approximately 8 percent) using Table E- 1 in Appendix E of the Paints lis ting risk analysis (U.S. EPA, 2001b).

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HGDB provides correlated hydrogeologic data for approximately 400 hazardous waste sites nationwide, and classifies these sites into the 12 hydrogeo logic environments shown in Table 2-3. Empirical distributions for hydrogeologic para meters associated with each of these environments are provided in Appendix F

. Table 2-3. Hydrogeologic Environments in EPACMTP

Code Description

01 Metamorphic and igneous

02 Bedded sedimentary rock

03 Till over sedimentary rock

04 Sand and gravel

05 Alluvial basins valleys and fans

06 River valleys and floodplains with overbank deposits

07 River valleys and floodplains without overbank deposits

08 Outwash

09 Till and till over outwash

10 Unconsolidated and semi-cons olidated shallow aquifers

11 Coastal beaches

12 Solution limestone Source: Newell et al. (1990)

Each of the 66 landfill locations included in th e risk analysis was assigned to one of the 12 hydrogeologic environments. Once the assignme nt was made, we conducted a preprocessing run with EPACMTP to construc t a set of randomly generated but correlated hydrogeologic parameter values for each landfill location. Meteorological Data. Running ISCST3 requires surface and upper air meteorological input data; we identified EPA’s Industrial Waste Air (IWAIR) model as a ready source of this information. As with the other models used in this risk analysis, IWAIR has been subjected to an extensive peer review process. To ensure intern al consistency in using these data, we matched the most representative IWAIR meteorological region with each of the 66 landfill locations. To determine which IWAIR meteorological region was most representative, we cross-referenced the landfill locations to EPACMTP climate centers . Next, the EPACMTP climate centers were cross-referenced to one of 57 IWAIR meteor ological regions (U.S. EPA, 2002a) shown in Figure 2-5 , with a single mete orological station representing each meteorological region. The final selection of the meteorol ogical station was based on two im portant considerations. First, overlapping contiguous areas were evaluated to determine whether they were similar enough that they could reasonably be represented by one meteorological station. Surface-level meteorological data (e.g., wind patterns), physiogr aphic features (e.g., mountains, plains), and land cover (e.g., forest) were all considered in th is determination. Second, characteristics of the respective climate center and the IWAIR mete orological region were considered, including industrial activity, population dens ity, location within the area, years of meteorological data available, and average wind speed. Additional details on the selection process are included in Appendix G

.

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Figure 2-5. Meteorological stations and regio n boundaries for the contiguous 48 states.

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## 3.0 Analysis
This analysis estimates risks from solvents disposed of in municipal landfills. Solvents can be released from landfills into the surrounding environment through several different mechanisms, including volatilization to the at mosphere and leaching fr om the landfill with subsequent migration through the unsaturated zone and into an underlying aquifer. For the analysis, we used computer-based models and sets of equations to estimate the risk to human health from current di sposal practices. These models include

 Source models that simulate the release of so lvent to air or in leachate from landfills

 Fate and transport models that estimate solvent concentrations in ambient air and groundwater

 Exposure models that estimate doses for humans exposed to solvents in ambient air, groundwater, and shower and bathroom ai r while showering with contaminated groundwater

 Risk models that calculate cancer and noncancer risks to humans.

This section describes the models and e quations used to calc ulate exposure point concentrations and risk. Section 3.1 describes the landfill source model used to predict the environmental release of solvents in disposed wipes and laundry sludge fr om municipal landfills. Sections 3.2
, 3.3 , and 3.4 describe the fate and transport models used to predict solvent concentrations in ambient air, groundwater, and shower and bathroom air, respectively. Section 3.5 describes the human exposure calculations. Section 3.6 describes the health benchmarks used to develop human risk estimates and describe s how these risks were calculated for human receptors. Supporting detail can be found in the following appendices:

 Appendix A , Risk Estimates, presents the solvent-specific risk estimates associated with a constant mass loading of 1 kg/yr for a seri es of percentiles ( 50th, 75th, 90th, 95th, and 99th).

 Appendix B , Chemical-Specific Parameters for Landfill Source and Shower Models, provides input parameter values and distributi ons used for chemical-specific parameters.

 Appendix C , Landfill Source Model Mathematical Formulation, presents the detailed formulation of the LFCR source model, including the underlying equations and the numerical solution algorithm.

 Appendix D , Landfill Source Model Testing, presents testing results for the LFCR source model, including model veri fication and robustness testing.

 Appendix E , Landfill Source Modeling Parameters, presents the source model input parameter values and distributions.

 Appendix F , Groundwater Modeling Parameters, presents the input values and the distributions used in modeling the groundwater pathway using EPACMTP.

 Appendix G , Unitized Air Concentrations (UACs) , describes the linear interpolation methodology used in determining the UAC values.

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 Appendix H , Equations, presents the equations that were used to estimate ambient air concentrations, shower air concentr ations, exposure (dose), and risk.

 Appendix I , Human Exposure Factors, documents the human exposure parameters.


### 3.1 Landfill Source Modeling and Characterization
This section describes the methodology and th e model (Lowry et al., 2008) used to predict the mass loss of solvent from the landfill to air and the unsaturated zone. Section 3.1.1 provides an overview of the conceptual model, including the basic approach and assumptions. Section 3.1.2 discusses the input parameters and va lues used in this risk analysis. Section 3.1.3 discusses the model outputs. Appendices B–E , listed below, provide additional information about the LFCR model and its implem entation for this risk analysis:

 Appendix B , Chemical-Specific Parameters for Landfill Source and Shower Models, provides input parameter values and distributi ons used for chemical-specific parameters.

 Appendix C , Landfill Source Model Mathematical Formulation, presents the detailed formulation of the source model, including the underlying equations and the numerical solution algorithm.

 Appendix D , Landfill Source Model Testing, presents testing results for the LFCR landfill source model, including model verification and robustness testing.

 Appendix E , Landfill Source Modeling Parameters, presents the source model input parameter values and distributio ns not presented elsewhere.

3.1.1 Conceptual Approach
The landfill source model estimates the mass loss of solvent from the landfill to air and the unsaturated zone for subsequent, media-sp ecific transport modeling. The LFCR source model was developed for this analysis as an altern ative to the General Soil Column Model (GSCM) (U.S. EPA, 1999a), the landfill model employe d by the 3MRA modeling system. The LFCR model addresses two requirements not consider ed in the GSCM approach: (1) the model is capable of simulating the effects of landfill gas on transport, and (2) the model framework reflects typical operating practi ces at municipal landfills. Gas pr oduction in landfills associated with waste biodegradation can lead to significant volatile ma ss loss and can be a dominant transport mechanism (Kjeldsen and Christen sen, 2001); however, GSCM does not include a flowing gas phase. There was also concern that the GSCM approach to waste addition (i.e., columns of annual waste) did no t capture potentially important transport proce sses (e.g., the volatile mass loss when smaller, daily volum es of waste are added to the landfill).

3.1.1.1 Overview This section provides a basic overview of the LFCR model design and capabilities. Appendix C provides a more comprehensive, mathema tical formulation of the model. The LFCR model is based on a fundamental mass balance approach, whereby chemical constituent mass is added to the landfill along with the waste and then undergoes mass transport and transformation within the landfill. Chemical mass ultimately remains within the landfill, degrades, or migrates from the landfill.

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The LFCR model describes landfill waste as a series of fully mixed reactors. This approach is an extension of the peer-revie wed Model for Organic Compounds in Landfills (MOCLA), which represents a landfill as a single r eactor with constant parameters (Kjeldsen and Christensen, 2001; Bartelt-Hunt et al., 2006). The LFCR mode l extends this approach by discretizing the landfill into a se ries of daily cells, each represen ting the volume of waste added during one day. 13 Unlike the MOCLA analytical model, LFCR is a numerical model allowing time-variable parameters. A separate, fully mixed reactor represents each daily cell. The daily cells are added to a landfill in a series of lifts, as shown in Figure 3-1. Each box in the figure represents a daily landfill cell, a nd each lift is shown in a differe nt color. Once an active landfill area (a landfill cell) has been covered with a lift , additional lifts are added vertically. A landfill may be divided into a user-specified number of equal-sized landfill cells, which are sequentially filled with waste. During the operating life of a landfill, the LF CR model adds new daily cells to the top of the next appropriate column as dictated by th e landfill-filling algorithm (Figure 3-1). Applying the approach described above generates a series of adjacent landfill columns. The mass loss from an entire landfill to the atmosphere and to the groundwater zone is then the cumulative contribution from all columns. The LFCR model adopts a mathematical descri ption based on fully mixed reactors and applies it within a framework consistent with typi cal landfill operations (daily cells added in lifts as depicted in Figure 3-1). Ma ss transfer occurs vertically between daily cells and the atmosphere and underlying groundwater zone. Mass transfer within ea ch column of daily cells is described mathematically using a model of coupled, fully mixed reactors in series. Figure 3-2 portrays such a column of daily cells and th e associated mass transfer processes. The LFCR model describes mass partitioning between the solid, wa ter, and air phases through linear equilibrium relations hips (Henry’s law and a solid-w ater distribution coefficient). First-order degradation can occur within the solid, water, and air phases as described by independent, phase-specific abiotic and biotic rate constants. (Note that only aqueous phase degradation was active for the solvents of concern in this analysis.) Diffusive transport can occur vertically betw een daily cells and the atmosphere and the underlying unsaturated zone. The LFCR model can describe diffusive transport through the following three types of cover, all of which were used for the analysis:

 A daily cover, which is typically 6 inches of native soil placed on the waste daily

 An intermediate cover, which is typically 12 inches of native soil placed when a landfill cell is completed and before the final cover is installed

 A final cover, which is placed over landfill cells at a user-specified time interval after the landfill cell operating life; the final cover can be either a soil-only layer or a composite layer consisting of a geomembrane and soil.

13 Although the model is designed for daily cells, it can accept cell time periods of other than 1 day. For example, to match the analytical solutions, most verification simulations (see Appendix D ) have a cell period of 30 years, with a single cell characterizing the entire landfill.

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a) First daily cell added to the bottom lift.

Each box represents one daily cell. Lifts are shown in different colors. The figures show waste addition over time until the landfill cell is full. The surface of each daily cell and landfill cell is a square. The daily cell depth can vary from the length (however, cubic daily cells are shown here). b) Daily cells added laterally in rows.

c) First lift completed. Second lift started. d) Filled to the fourth lift, fourth row, third cell.

e) Filled to the fifth lift, sixth row, second cell. f) Landfill cell filled through the seventh lift.

Figure 3-1. Model representation of op erational landfill waste addition.

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### Q
w 

### S λ n



### J

D,n-1 

# C
n



### J Qw,n-1 

### J

Qa,n-1 

# ∑

n

ai i=1 

### Q



### Q a1



### J Do 

### J Qwo 

### Q
w 

### S λ 1 

# C 1 

### Q
w 

### S λ 3 

### J

D,n



# C 3 

### J

Qa,n



# . .

. . .

.

Q
w 

### S λ 2



### J

D1 

# C 2



### J Qw1 

### J

Qa1 

### J D2 

### J Qw2 

### J

Qa2

Reactors in blue; cover and liner layers in red. C i =daily cell concentration; S

li =degradation sink term; Q ai

=landfill gas flow rate; Q w = water infiltration rate; J

Qa =Flux in flowing air; J D =Diffusion flux (in air and water); J Qw

=Flux in flowing water 3

ai i=1 

### Q 

# ∑

2

ai i=1 

### Q 

# ∑

Figure 3-2. Coupled in-series reactor mo del of a column of landfill cells.

The LFCR model also describes diffusive tran sport with no cover while waste is actively being added. The no-cover diffusive sink term relie s on the user-specified fraction of a day with no cover, the working face angle, the waste sublay er thickness, and an analytical solution to the diffusion equation (see Appendix C for details).

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The following liner configurations are possibl e in the LFCR model (only the no liner and composite liner options were used for this analysis):

 No liner

 Soil liner

 Composite liner consisting of a geomembrane and soil.

Linear-equilibrium sorption to the geomembr ane can be described using a permeation coefficient, which captures the effects of so rption to the geomembrane surface and diffusion through the geomembrane (Sangham and Rowe, 2001) . Linear-equilibrium so rption to the liner and cover soils can be described by dividing the diffusion coefficient by a user-calculated retardation coefficient. This analysis did not in clude sorption within the cover/liner soil layers. Advective transport occurs in landfill gas generated through biodegradation of landfill waste and in water infiltrating through the landfil l (leachate). The model represents upward gas and downward water advec tive transport thr ough advective fluxes betwee n reactors and leaving the system through the top and bottom reactors of each column. An exponential decay function similar to that of the Landfill Gas Emissions Model (LandGEM) predicts the landfill gas generation rate (U.S. EPA, 2005a). The model simu lates infiltration condi tions before and after the final cover is applied with user-specified average infiltration rates. In addition, the user specifies a liner leakage rate (bef ore and after the final cover) that describes the rate of advective flow through the landfill liner (diffusion through the liner is described as a separate process). The infiltration and leakage rates may be different, fo r example, if a leachate collection system is present and collects water from the bottom of the landfill after it has flowed through the waste and before it leaks through the liner. For cas es with no liner (and thus no leachate collection system), aqueous advective transport to the groundw ater zone is based on the net infiltration rate (the liner leakage rate is not used). Mass transfer to the unsaturated zone equals the sum of the diffusive and advective transport from the landf ill bottom. Mass transfer to the atmosphere equals the sum of advective transport in landfill gas, diffusive transport through the top cover, and diffusive transport with no cover. The source model numerical solution was based on a Runge-Kutte algorithm for solving systems of linear differential equations. The so lution time step varied as a function of the solution (smaller time steps are required to accurately solve s cenarios with greater rates of change). Post-processing of the source model re sults generated time series of annual average flux rates (g solvent/yr-m 2 ) from the landfill to air and the unsaturated zone. These time series became input for the air and groundwater fate a nd transport models. The annual mass loss values to air and the unsaturated zone from the source model were averaged ove r the total surface area of the landfill, even though the footprint of th e landfill increased over time until all landfill cells contained waste.

3.1.1.2 Key Assumptions The LFCR model is based on a relatively simp le conceptualization of physical/chemical processes in a landfill. Although much more complex models of landfills have been developed, the LFCR model describes the mechanisms believe d to be of primary importance for this national-scale risk analysis. The model ach ieves a balance between model complexity, uncertainty, and implementability. A more comp lex model would require more extensive and detailed input parameters, many of which are no t available on a national scale. In addition, the

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variability and uncertainty in landfill conditions both within a single landfill and among different landfills makes the use of a more detailed, mech anistic model inappropriate for this analysis. Furthermore, greater complex ity would likely lead to addi tional runtime requirements and implementability concerns. This section lists some of the recognized model assumptions and limitations that were accepted in balancing model complexity, uncertain ty, and implementability. Note that the LFCR model framework could be extended to account fo r many of these more complex behaviors (e.g., additional heterogeneous and time-variable para meters). However, such complexity was not justified or supported by data for this analysis. Source mode l assumptions and limitations included the following:

 The model allows vertical but no lateral tran sport. Diffusion can occur in both vertical directions, while landfill gas migrates upw ard and water flows downward. Although this approach ignores lateral flow within a landfill, the comp lexities of 3-D flow (e.g., heterogeneities, preferential flow paths) would make a multidimensional model too complex for this analysis. In addition, there is a clear precedent for assuming 1-D transport within landfill transport models a nd risk analyses (e.g., Jury et al., 1983, 1990).

 The model assumes long-term average and homog eneous moisture conditions within the waste, liner, and cover layers. The mode l does not provide a dynamic water balance model to simulate varying moisture cont ent in response to short-term weather fluctuations. Long-term average moisture cont ent values are consid ered appropriate and sufficient to describe behavior at time scales ranging from decades to hundreds of years. Furthermore, an accurate water balance mode l would need to consider the complexities of water movement within landfills (e.g., pr eferential flow, heterogeneity, short-term response to rainfall). The complexity and parameterization of such a complex water balance model would be very challenging fo r a national-scale anal ysis and might make the solution intractable.

 Without a dynamic water balance, infiltration flow rates in the LFCR model are equal to leaching flow rates (water in equals water out). There is no change in water storage within the landfill, and the model assumes l ong-term average moisture conditions within the waste and within the cover and liner so ils. This assumption deviates from observed behavior in some landfills, whereby the waste moisture content increases over time after application. The maximum leaching rates occur only after the waste has reached the field capacity moisture content. Nevertheless, there is experimental evidence that the field capacity is exceeded over long time scales, wi th water infiltration moving readily through preferential flow channels and exiting the landf ill as leachate at a similar rate as the infiltration. For example, the graph in Figure 3-3 from Fungaroli and Steiner (1979) shows measured infiltration and leachate in landfill waste over an approximately 4.5 year timeframe. As the graph shows, leachate fl ow is much less than infiltration during approximately the first year and a half as the waste saturates to field capacity. After that, the leachate flow rate essentially mimics the infiltration rate, suggesting that field capacity moisture content in the waste is maintained over a long time scale and that infiltration is flowing readily within the pr eferential flow pathways. The Fungaroli and Steiner (1979) results are based on an expe riment performed at a scale smaller than typical landfills (specifically, an 8-foot deep waste column); therefore, extrapolation of these results to full-scale landfills introduces some uncertainty. Additional experimental evidence is available from landfill tracer st udies, which show early time breakthrough,

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with the maximum concentrations occurri ng shortly after breakthrough (Kjeldsen and Christiansen, 2001). Therefore, water flow in the LFCR model appear s to represent longterm behavior observed in landfills reasona bly well. However, there likely are some landfills where the waste moisture content re gularly fluctuates below the field capacity over longer timeframes, and a more comple x water balance would be necessary to represent the flow dynamics more accurate ly. In those cases, the LFCR approach (infiltration equals leachate fl ow) is a less realistic approx imation that may overestimate the mass loss in the leachate. Conversely, this approach may overestimate aqueous biodegradation losses by over predicting the ma ss in leachate. On balance, the approach is believed to provide a more conservative es timate for cases where the moisture content is below field capacity. Given the uncertainty and the significantly greater complexity associated with a dynamic water balance model, the more conservative and simple approach was selected.

Figure 3-3. Measured infiltration (water adde d) and leachate flow rates over time (from Fungaroli and Steiner, 1979).

 The source model uses long-term average inf iltration values before and after the final cover is applied. The model does not incorpor ate short-term temporal variations in infiltration (e.g., due to rainfall events). Tran sport processes can be strongly influenced by the infiltration rate (most significantl y, aqueous advective transport). Thus, the transport may be under - or over predicted re lative to fluctuations over shorter time scales. However, long-term average rates are believed to provide a good approximation over the long time scales of interest for this analysis.

 Flow patterns within landfills can be complex, with channeled flow and low permeability zones leading to some waste that essentia lly never contacts flowing water. The LFCR approach assumes equilibrium partitioning be tween the air, solid, and aqueous phases of

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the waste, thus neglecting any potential kinetic mass transfer limitations associated with relatively less permeable waste and channeled flow. This simplification could lead to relatively conservative mass loss estimates, wh ereby mass more readily migrates from the landfill waste into the mobile air and aqueous phases. More complex flow behavior was considered during LFCR model development; however, the simpler approach was selected to satisfy project requirements, in cluding a short runtim e for the Monte Carlo analysis, the availability of input paramete rs, and implementability given the available schedule and resources.

 Gas flow occurs vertically in the model w ith no dependence on the waste permeability. It is well known that gas flow can be inhibite d where waste has an increased moisture content. Heterogeneous moisture conditions can lead to complex air flow dynamics within landfills, including la teral components to flow. Gi ven the model objective of describing long-term average conditions at a na tional scale, detailed air flow dynamics in the landfill were not considered.

 The model assumes a single long-term av erage waste temperature. Although the temperature of waste varies over time, no know n, relatively simple models are available to describe this complex process. The wast e temperature is used to adjust diffusion coefficients and Henry’s law c onstants for temperature effects. If the waste temperature is over predicted, diffusive and vapor advective lo sses would be over predicted. In general, landfill waste temperatures should decrease over very long time scales as biodegradation subsides. Because most vapor-phase mass lo ss occurs relatively early in the landfill lifetime, it is believed to be accurate to a ssume a constant, average waste temperature representative of the activ e biodegradation period.

 The model assumes that landfill gas is 50 per cent methane, which is consistent with Bartelt-Hunt et al. (2006). Incr easing this percentage would lead to less advective vapor mass loss. Alternative sources for this ratio were not identified.

 Due to significant uncertainty, no attempt wa s made to correlate biodegradation rates with the waste moisture content, even though conditions with greater moisture are known to be more conducive to biodegradation. As an alternative, the analysis used the single, most conservative available valu e for the biodegradation rate.

 The model does not simulate the formation of daughter products from the degradation of chemical constituents in the landfill. If biodegradation is significant and daughter products have greater to xicity than the constituent analy zed, this exclusio n could lead to an under prediction of risk. For example, the solvents tetrachloroethylene and trichloroethylene can biodegrade into vinyl chloride, which has a greater toxicity than the parent constituents.

 The model does not simulate water evaporation or chemical constituent volatilization from water within the cover or at the atmo sphere/cover interface, which could lead to under prediction of mass loss. However, if evaporative mass loss is limited by aqueous diffusion, the magnitude of the under predicti on would be very small (rates of aqueous diffusion are orders of magnitude smaller than rates of vapor diffusion). If wicking (upward flow of water in the cover soil driven by water evaporation and capillary forces) is significant, the unde r prediction would be somewhat gr eater. Incorporation of a soil evaporation model would enhance the mode l by capturing this potential chemical constituent release mechanism from the wast e. Nevertheless, the magnitude of such

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losses is thought to be small relative to the dominant fate and transport processes for solvents evaluated in this study (typically bi odegradation and advective vapor transport).

 The model assumes instantaneous and revers ible equilibrium between the solid and aqueous phases (sorption/desorption), so that the concentration ratio between the solid and aqueous phases is constant (and equa l to the distributi on coefficient). Nonequilibrium sorption processes likely limit mass transfer between the aqueous and solid phases in landfills to some unknown degree. To the extent that non-equilibrium desorption limits mass loss from the solid pha se in landfills, the LFCR may over predict mass loss.

 The model adopts a linear sorption model. Nonlinear sorption typi cally becomes more significant at concentrations that are considerably higher than the constituent concentrations predicted at the mass load ing rates of concern for wipes and laundry sludge disposal. Therefore, a linear sorption model was considered appropriate for this analysis.

 The model considers the influence of so rption on transport th rough the cover/liner geomembrane layers. However, the model assu mes that sorption is inactive within the cover/liner soil layers. We made this assumpti on because (1) we were unable to identify a generalized approach to estimate the cover/lin er soil retardation coefficient for national conditions, and (2) significant uncertainties and unresolved questions remain in the science of transport within cover/liner soil layers. For example, sorption to mineral surfaces can be significant within clay rich so ils; thus, the classic approach of estimating sorption parameters from the soil organic carb on fraction is often insufficient (Itakura et al., 2003). Bartelt-Hunt et al. (2003) demonstrated for engi neered soils that sorption increases with the organic carbon fraction only up to a certain percentage (approximately 50 percent of the cation excha nge capacity). Above that car bon content, the rate of sorption actually decreases, likely due to a decreasing available mineral surface area. Another noteworthy complication involves the dependence of estimated sorption parameters on experimental conditions. Severa l researchers have found that the measured distribution coefficient is gr eater for batch versus column experiments, and the value decreases with the solids concen tration in batch experiments (Itakura et al., 2003; Kim et al. 2003; Khandelwal and Rabideau, 2000). A nother complexity is associated with diffusion and sorption behavior in unsaturate d soils and a dependence on the moisture content (Steinberg et al., 1999). Some invest igators have documented increasing rates of diffusive transport as the moisture content in creases, likely due to decreasing availability of mineral surfaces for vapor sorption (C abbar and Bostanci, 2001). In other cases, increasing the moisture content decreases rates of diffusion, likel y due to increased vapor-phase tortuosity (Aubertin et al., 2000). Given the sign ificant uncerta inties and the absence of a generalized approach for descri bing cover/liner soil so rption, this analysis assumed no sorption within cover/liner soil s. This assumption could lead to more conservatism, in that relatively greater c onstituent mass could mi grate through the landfill cover and liner. However, in many cases, the co ver/liner soil consists of an engineered clay primarily containing sodium bentonite w ith relatively little sorptive capacity (Lake and Rowe, 2005). Increased sorption within such engineered clays is possible if a fraction of the bentonite is replaced with organocla ys or activated carbon. However, given the relatively small thicknesses of such engineer ed soil liners, the impact on contaminant migration is relatively small (Lake and Rowe, 2005).

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 Leachate is sometimes managed (and possibl y aerated) in impoundments, which could release volatile constituent mass to the air. This analysis did not consider this leachate handling process, which would require use of an additional source model describing mass loss from surface impoundments. Section 4 provides further eval uation of the potential significance of this exclusion, where it is shown that the re sults would not have been significantly changed if this mechanism had been included.

 After collection, landfill leachate is sometimes applied to the top of activ e landfills. The LFCR model does not consider th is potential mass transfer into the landfill. The potential significance of leachate applica tion to the landfill surface can be evaluated by considering the fraction of total mass loss due to aqueous advective transport (leaching) as shown in Section 3.1.3 (Table 3-5). In only one case (methanol) does the average mass loss fraction associated with leaching exceed 10 percent of the total mass loss. The leaching mass loss percentage is less than 2 percent for a majority of solvents. Because many of these solvents would atte nuate in a leachate impound ment through biodegradation, volatilization, and other mechan isms, the percentage contri bution associated with any reinfiltrated leachate would be smaller. Ther efore, the mass fraction reinfiltrated to the landfill would be less than 10 percent for methanol and much less than 10 percent for the other solvents. Given the relatively small percentage mass loss associated with leaching and the probable attenuation of mass in the impoundment for most solvents, the wipes and laundry sludge results would not change significantly if ma ss transfer associated with leachate reinfiltration were considered.

3.1.2 Model Inputs This section provides a description of the de velopment of input parameters supporting the landfill source modeling for this analysis. Appendices B
, E , and F present the values and distributions for LFCR input parameters, along with descriptions of the basis and sources for values. This section provides a summary of these input data, as well as data development details not included in the appendices.

3.1.2.1 Infiltration and Liner Leakage Rates

The source model infiltration ra te describes the rate of dow nward vertical water flow through the landfill, leading to aqueous advective mass transfer between daily cells, as well as mass loss with leachate exiting the landfill. For the no-liner case, this advective mass loss is transferred to the unsaturated zone. For cases with a liner, advective mass loss is recovered by a leachate collection system, except for the mass speci fied by a liner leakage rate (the flow rate through the liner). The average in filtration rate can change signi ficantly once the final cover is installed. For example, if the final cover incl uded a geomembrane, whereas the interim cover consisted of native soil, the infiltration rate woul d decrease significantly once the final cover was placed. The LFCR source model accepts as input the infiltration rate and th e liner leakage rate before and after the final cover is installed. Values for infiltration and liner leakage rates were taken from the existing EPACMTP regional, site-based database (U.S. EPA, 2003a). The EPACMTP database was originally developed to represent national variability for use in Monte Ca rlo risk analyses. The EPACMTP database provides “regional, site-based” parameter values, whereby physically unrealistic parameter combinations are avoided in the repr esentation of national va riability. For example, the database would not combine a shallow depth to groundwater similar to conditions in Florida

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with an arid-climate recharge rate similar to conditions in the southwestern United States. Infiltration rates within EPACMTP were deve loped from Hydrologic Evaluation of Landfill Performance (HELP) simulations and empirical data. The EPACMTP system has been peer reviewed and used for several risk analyses over the past 15 years. The EPACMTP Technical Background Document (U.S. EPA, 2003c,d) and Parameters and Data Background Document (U.S. EPA, 2003a) provide details about the development of EPAC MTP. The reader is referred to these documents for additional background in formation. Further details about EPACMTP are provided below only in cases where the data deve lopment diverges from these documents. Note that the EPACMTP groundwater transport m odel was used in this analysis (see Section 3.3 ). Input values for subsurface modeling parame ters common to both the LFCR and EPACMTP were shared, thereby providing consistency with the EPACMTP regional site-based framework. Values for infiltration and liner leakage rates correspond to the EPACMTP database infiltration rate parameter SINFIL (U.S. EPA, 2003a). Two sets of SINFIL values are available in EPACMTP to represent conditions with (1) a soil-only cover and no lin er, and (2) a composite cover and liner. EPACMTP no-liner infiltration rates were derive d from HELP simulations that considered climatic and site-spe cific variability (e.g., rainfall a nd soil type) and a soil-only cover (U.S. EPA, 2003a). EPACMTP infiltration rates representative of a closed landfill with a composite liner were derived from empirical liner leakage data. For this analysis, a modification was made to the empirical liner leakage rate distribution relative to the approach documented in U.S. EP A (2003a). The liner leakage rates were based on monthly average leak detection system (LDS) fl ow rates as reported by TetraTech (2001). The infiltration data in the TetraTech report cover five general lin er configurations:

 GM: A high-density polyethylene geomembrane over native soil

 GM/CCL: A 1- to 1.5-mm thick geomembrane overlying a 3-ft thick compacted clay liner

 GM/GCL: A 1- to 1.5-mm thick geomembrane overlying a 6-mm thick geosynthetic clay liner

 GM/GCL/GM: A 6-mm thick geosynthetic clay liner between two 1- to 1.5-mm thick geomembranes

 GM/GCL/CCL: A 1- to 1.5-mm thick geomembran e overlying a 6-mm thick geosynthetic clay liner and a 3-ft thick compacted clay liner.

Due to the potential for water to be released during the consolidati on of a compacted clay liner (yielding an unknown contribution of water to the LDS flow rate), the compacted clay liner leakage data (GM/CCL and GM/GCL) originally were not used (U.S.EPA, 2003a). However, release of water during consolida tion would likely be less significan t at higher infiltration rates, which would correspond to the upper end of the risk distribution. For this reason, EPA chose to retain the compacted clay liner da ta in developing the liner leakag e distributions for this analysis. However, the geomembrane (GM) data were excluded from the leakage rate distribution, because the geomembrane liner type is inconsis tent with requirements for newly constructed landfills. Inclusion of compacted clay liner data and re moval of geomembrane liner data resulted in an infiltration rate distribution based on 53 data points. These data were further screened to exclude data points from monito ring studies less than 6 months long, as directed by EPA. Six

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months was selected as a reas onable screening criteri on above which the results of monitoring studies would not be expected to be uniforml y influenced by seasonal variations (and hence would not unduly affect the distribution of LDS flow rates). After applying the study duration criterion, the final data set consis ted of 50 data points used to de fine the composite-liner leakage distribution. Summary sta tistics for that distribution are presented in Appendix F (Table F-3). Before the final cover is installed, landfills are often covered temporarily with soil. Because the EPACMTP no-liner inf iltration rates represent landfills with a soil-only cover, these rates were used for conditions before the final cover in both the lined and unlined landfill scenarios. After installation of the final cover, the EPACMTP composite-liner rates were used for the lined landfill scenario, and the EPACMTP no-liner infiltration rates were used for the unlined landfill scenario. Landf ill leakage rates in the source model corresponded to the EPACMTP composite liner rate s throughout the simulation. In the unlined scenario, liner leakage rates were not used, because all of the leachate flowing through the landfill migrates to the underlying groundwater. In 14 percent of the 10,000 realizations, SINFIL (composite liner) exceeded SINFIL (no liner) in the EPACMTP databa se. To maintain data consistency with the groundwater modeling simulations fo r the lined landfill scen ario, the infiltration rate before the final cover was set to SINFIL (composite lin er) rather than SINFIL (no liner) for those realizations.

3.1.2.2 Cover/Liner Characterization Parameters The LFCR model describes diffusive transport through various potential cover materials, including daily, intermediate, final soil, and fi nal composite (soil and geomembrane) covers. This analysis considered two liner scenarios: a composite-liner scenario and a no-liner scenario. Each of the cover and liner type s requires the following input para meters to describe diffusive transport through the respective la yers: soil porosity, moisture c ontent, and thickness. Diffusion through a geomembrane also requ ires specification of a permea tion coefficient, which is tabulated and described in Appendices B and C . The permeation coefficient captures the effects of geomembrane sorption and diffusion. Sorption to the cover/liner soil layers was assumed to be negligible (see Section 3.1.1.2 ).

Cover and liner thicknesses were consistent with values specified in Subtitle D regulations (e.g., a liner 2 feet th ick). Soil parameter values (moi sture content, porosity) were from the EPACMTP database, which includes a di stribution of three soil types designed to represent national variab ility (U.S. EPA, 2003a). Table 3-1 presents the effective porosity and field capacity values associated with these EPACMTP soil types. The porosity and moisture content for the daily, intermediate, and final cover (no-liner scenario, soil- only cover) were set to the effective porosity and field capacity associ ated with the EPACMTP soil type. The field capacity was considered a reasonable estimate of long-term average moisture conditions for these soils (Bartelt-Hunt, 2006). The porosity for the clay layer within a composite cover and liner was set to the porosity of liner soil in the HELP documentation (Schroeder et al., 1994). The moisture content for the clay layer within a composite cover and liner was equal to the average of liner moisture contents reported in Benson et al. (1999).

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Table 3-1. Characteristics of EPACMTP Soils

Soil Type EPACMTP Soil ID HELP Soil Number Total Porosity Effective Porosity Field Capacity Wilting Point Saturated Hydraulic Conductivity

(cm/sec) Sandy Loam 2 6 0.453 0.41 0.19 0.085 0.00072 Silty Loam 1 9 0.501 0.45 0.284 0.135 0.00019 Silty Clay Loam 3 12 0.471 0.43 0.342 0.21 0.000042
Porosity and moisture content values expressed as volume fractions (volume void/water divided by total volume).

3.1.2.3 Hydrogeologic Setting Parameters The EPACMTP database provided hydrogeologic setting parameter values, including the depth to groundwater (EPACMTP parameter DSOI L, used for the unsaturated zone thickness) and the EPACMTP regional soil type. The EPACMTP regional soil type comprises the three soils listed in Table 3-1 in a di stribution representative of nati onal conditions (U.S. EPA, 2003a). The unsaturated zone porosity and moisture cont ent were equal to values consistent with the EPACMTP regional soil type.

3.1.2.4 Landfill Characterization Parameters Each landfill simulation includes a series of sequential landfill cells. The landfill cell operating life (time period of waste application for one landfill cell) of 3 to 5 years is consistent with typical industry practice (Barlaz, 2006). Th e total landfill operati ng life values were adjusted to the nearest integer evenly divisible by the landfill cell operating life, so that a whole number of landfill cells make up a given landfill (i .e., no partial cells). The time period before a final cover is installed is also c onsistent with typical industry pract ice in that there is typically a delay before landfill managers install the final co ver (Barlaz, 2006). The daily cell/lift height describes the thickness of the ac tive waste layer and is derived fr om Aivaliotis et al. (2004) and typical industry pract ice (Barlaz, 2006). The fraction of day without a cover describes the time when waste is being added to the active daily cell before the daily cover is applie d. Typical industry practice for this stage is 8 hours (Barlaz, 2006). The daily waste sublayer period describes the time period during which a sublayer of waste is added to a daily cell. The no-cover mass loss formulation (see Appendix C ) uses this parameter to describe mass loss from layers as waste is added throughout the workday. A value of 1 hour was selected based on profe ssional judgment (Barlaz, 2006); however, model testing did not indicate that the model was very sensitive to this parameter. The no-cover mass loss formulation also uses the angle of the worki ng face to the horizontal. Aivaliotis et al. (2004) provided a distribution of values for this angle, although testing results showed that the model was also not very sensitive to this parameter. Several EPA-sponsored modeling analyses have focused on smaller landfills associated with an Industrial D database (U.S. EPA, 2003a ,b,c). For the current analysis, a more recent database that EPA obtained commercially from the Waste Business Journal (WBJ) and analyzed (referred to here as the EPA-WBJ database) was considered a more representative data source for municipal landfill geometry and operating life charac teristics. Of special significance given the design architecture of the LFCR model was the requirement that average landfill depths be realistic and consistent with known operating pr actices (e.g., lift height s). However, due to copyright concerns, EPA was only able to provi de summary indicators from the EPA-WBJ data

Do not cite or quote. 3-14Review Draft Section 3.0 Analysis
(rather than data on specific landfills directly from the original WBJ database). This created some challenges in constructing a data set that co uld be used by the model, as discussed further below. EPA’s analysis of the WBJ database suggest ed that the landfill parameters could be described with lognormal distributions. Therefor e, EPA provided the associated parametric statistics for the following variable s contained in the database: area (m 2 ), operating schedule (days/yr), and daily intake rate (tons/day). EPA also derived a number of variables from the reported data and calculated lognormal parametric statistics for these variables, as follows: operating life (yrs), total capacity (tons), total landfill volume (m 3 ), and landfill depth (m). Unless start and closure dates were both repor ted, the operating life wa s derived assuming the reported operating schedule and dail y intake rate throughout the life of the landfill. The derived total capacity assumed the same operating schedule and waste intake rate from the reported start date to the present (and then added the re maining capacity). The derived landfill volume assumed a compacted wet waste density of 1,160 lbs/yd 3 . Average landfill depth was simply the volume divided by the area. EPA also developed several uni- and multivariate parameter correlations, including descriptions of the re lationship between capacity and intake, area and intake, area and capacity, area and depth, area an d operating life, and intake and operating life. Notably, there was no significant correlation between landfill depth and landfill area.

The statistical information described above wa s used to derive preliminary combinations of landfill characteristics. However, several initial parameter values and combinations were unrealistic and, in many cases (more than 10 percen t), inconsistent with the summary statistics from the EPA-WBJ database. For instance, the resulting range of landfill depths (0.0026– 1,412 m) was significantly outside of the range of depths in the EPA-WBJ database (0.5–115 m). Similarly, the landfill operating li fe distribution contained unrealistic values (e.g., thousands of years). Consequently, an alternative approach was developed that could

 Create a representative set of landfills that we re realistic from an engineering standpoint (i.e., parameter values were calculated as appropriate from key landfill variables)

 Avoid producing any parameter values outsi de the realistic ra nge defined by the EPA-WBJ data

 Provide positive correlations for parameters shown to be positively correlated in the EPA-WBJ database (e.g. , area and capacity)

 Allow for comparison with other data sources , recognizing that some data sources focus on specific subsets of MSWLFs

 Reflect the operating assumptions (minimum height) associated with the landfill source model.

The approach was initiated by using the statisti cal descriptors for parametric distributions (assumed to be lognormal) provided by EPA to create distributions of 15,000 values for three basic landfill parameters:

 Area (as reported in the EPA-WBJ database)

 Depth (derived from EPA-WBJ database)

 Operating life (derived from the EPA-WBJ database).

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To accommodate height requirements of the la ndfill source model (2-ft final cover, 2-ft liner, 6-inch daily cover, and a minimum lift height of 4 ft), the minimum landfill depth was set to 2.9 m, which was approximately the 25th perc entile in the distribution created using the EPA-WBJ database. 14 This minimum shifted the landfill depth distribution upward, which was more consistent with other data sources and e xpert recommendations (e .g., EREF, 1999; Barlaz, 2006). Using the distributions for landfill area (m 2 ), landfill depth (m), a nd operating life (yr), we calculated values for three other parameters as follows:

 Landfill volume (m 3 ) = landfill area (m 2 ) x landfill depth (m)

 Landfill capacity (tons) = landfill volume (m 3 ) x wet density of the waste (ton/m 3 )

 Daily waste intake rate (tons/d) = landfill capacity (tons) x operating life (yr)/operating days/yr.

The wet density of the waste was assumed to be 0.801 ton/m 3 (Barlaz, 2006). A 6-dayper-week (312 days per year) operating schedule was assumed (the predominant operating schedule in the EPA-WBJ database). Paramete r distributions of 15,000 landfills were created. Values outside of the EPA-WBJ minima and ma xima were excluded, resulting in approximately 1 percent (201 of the 15,000) being eliminated. Ne xt, correlations between parameters were compared with correlations from the EPA-WBJ data. For example, the correlation coefficient between the daily waste intake rate and the cap acity in the constructed data set was 0.9, as compared with a correlation coefficient of 0.93 in the EPA-WBJ database. Similarly, the correlation coefficient between area and capacity was 0.6 for the constructed landfill data set and 0.53 for the EPA-WBJ database. Thus, the strength of the correlations between critical landfill parameters was very similar in the construc ted landfill data set and the EPA-WBJ database. Finally, basic statistics (average, minimum, ma ximum) from the constructed data set were compared with those from other sources (ERE F, 1999; Themelis and Ulloa, 2005; U.S. EPA, 2006a), to take into account the f act that some of the other sour ces focus on different subsets of landfills. For instance, EPA’s Landfill Methane Outreach Program (LMOP) database (U.S. EPA, 2006a) and the Themelis and Ulloa (2005) data focus on MSWLFs having landfill gas energy potential, and thus tend to include larger landfills. The comparison shown in Table 3-2 suggested that the construc ted landfill data set tends to have somewhat larger areas and somewhat sha llower depths when compared with other recent data sources. In addition, relative to the EPA-WJB database, the constructed data set generally shows smaller variability in the parameter dist ributions. These differences notwithstanding, the similarity in correlations, value ranges, and averages strongly s uggests that the constructed data set achieves all five goals described above and provides reasonable repres entations of MSWLFs for use with the LFCR model. Appendix F (Table F-3) provides the cumulative distribution functions (CDFs) for the constr ucted data set parameters.

14 Note that the preferred length unit for this analysis was meters; however, measurements are sometimes presented in English units, given the common usage of this measuring system in source data.

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Table 3-2. Comparison of Constructed Data Set with Other Sources

Statistic EPA-WBJ Wipes Constructed

Data Set EREF (1999) Themelis and Uloa (2005) EPACMTP (U.S. EPA,

2003a) LMOP
Area (m 2 ) Avg 848,784 770,464 566,856 5,674,167 522,521 512,824

Max 24,281,238 23,849,771 1,809,009 57,460,000 9,342,000 8,781,990

Min 4,047 4,072 141,645 170,000 4,010 4,047

SD 1,441,143 1,280,745 380,466 - - 705,339

N 1,639 14,799 44 36 - 1,046
Depth (m) Avg 10.6 18.2 24.1 28 3.6 28.9

Max 115 114.8 48.9 53 10.1 132.7

Min 0.5 2.9 8.5 14 0.6 2.4

SD 15.7 19.2 8.9 - - 20.2

N - 14,799 44.0 36 - 468.0
Volume (m 3 ) Avg 10,096,000 12,062,539 13,646,339 158,719,051 1,894,461 14,843,695

Max 484,218,056 305,127,109 88,462,272 3,045,380,000 93,980,520 1,165,150,523

Min 124 22,093 1,204,875 2,380,000 2,446 9,875

SD 26,124,739 21,894,169 - - - - N 1,829 14,799 - - - - Capacity (tons) Avg 7,658,940 9,656,343 10,924,211 127,058,285 1,516,560 11,882,722

Max 367,333,317 244,261,335 70,816,103 2,437,897,397 75,233,588 932,730,046

Min 94 17,686 964,530 1,905,245 1,958 7,905

SD 19,818,524 17,526,790 - - - - N 1,829 14,799 - - - - Active life (yr) Avg 44 42.2 - - - - Max 200 194.2 - - - - Min 3.9 7.6 - - - - SD 20 18 - - - - N - 14,799 - - - - Daily Waste Intake (tons/day) Avg 457 832 - - - - Max 13,014 11216 - - - - Min 0.01 0.90 - - - - SD 1,013 1472 - - - - N 2,411 14,799 - - - -

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3.1.2.5 Simulation Parameters Several parameters control the model si mulation, including the maximum allowed simulation time and error tolerances. Appendix E provides values and descriptions for these parameters. There is a balance between the Rung e-Kutte truncation error tolerance, the solution mass balance, and runtime, whereby the mass bala nce accuracy and runtime decrease as the error tolerance increases. The error tolerance was set so that the mass balance error was maintained below 1 percent for all robustness-testing simulations.

3.1.2.6 Waste Characterization Parameters A series of parameters characteri zing the landfill waste appears in Appendix E . The landfill waste temperature is generally elevated as a result of biodegradation. The average waste temperature is used to adjust diffusion coeffi cients and Henry’s law constants for temperature effects. The model accepts a single, average te mperature, although the te mperature in landfills does decrease over time as biodegr adation subsides. Nevertheless, this approach was chosen because of the unavailability of simple models to describe temperature fluctuations over time. Also, volatile solvents are often released relatively early in a landfill’s life, when biodegradation is active and temperatures are generally elevated. The distributions for waste bulk density and vo lumetric moisture content were calculated based on values of wet density and the gravimetri c moisture content typical of municipal waste, assuming a water density of 1,000 kg/m 3 (Barlaz, 2006). The waste moisture content distribution was correlated with the infiltration rate with a correlation coefficient of 0.85 to reflect the influence of climatic variation on moisture. The waste porosity distri bution is typical for municipal waste and was based on Qian et al . (2002) and Barlaz (2006). The porosity was correlated inversely with bulk dens ity (correlation coefficient of -0.75) to reflect that the bulk density tends to increase as the porosity decr eases. The distribution for waste organic carbon fraction is typical for municipal waste and was based on Bartelt-Hunt et al. (2006) and Barlaz (2006).

The solvent concentration in the waste was set to a constant value of 1 kg/m 3 for the landfill source model simulations. Note that this is a total volumetric concentration including the mass in the solid, aqueous, and vapor phases (see Appendix C for details). Because the analysis results were expressed in terms of mass loading limits rather than concentrations, the results were scaled linearly to maintain a constant mass loading rate (see Section 5 ). The model linearity allows for this straightforward adjustment of the results. The LFCR model uses an exponential decay model similar to LandGEM to describe landfill gas generation (U.S. EPA, 2003a). The controlling paramete rs are the gas generation rate and the potential methane generation capacity. The gas generation rate is known to vary with moisture conditions, because biol ogical activity generally increases with moisture. Therefore, a relationship was developed between the infiltrati on rate and the gas ge neration rate based on Barlaz (2006), Bartelt-Hunt et al. (2006), and the AP-42 user’s manual (U.S. EPA, 1997a). The resulting piecewise linear function provides a me thod for calculating the gas generation rate from the infiltration rate as presented in Table 3-3 and Figure 3-4 . The potential methane generation rate distribution was based on the AP-42 use r’s manual (U.S. EPA, 1997a) and Barlaz (2006). The model assumes that landfill gas is made up of 50 percent methane, which is consistent with Bartelt-Hunt et al. (2006).

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Table 3-3. Relationship Between the Infiltration Rate and the Gas Generation Rate

Infiltration Rate (m/yr)

Min Max Gas Generation Rate (/yr) Basis

< 0.02 = 0.01 Minimum value of 0.01 (Barlaz, 2006)

0.02 0.04 Infiltration / 2 AP-42 arid climate rate (U.S. EP A, 1997a), Bartelt-Hunt et al. (2006)

0.04 0.12 0.25 x Infiltration + 0.01 AP-42 wet climate rate (U.S. EPA, 1997a), Bartelt-Hunt et al. (2006)

0.12 0.36 0.25 x Infiltration + 0.01 Li near relationship (Barlaz, 2006)

> 0.36 = 0.1 Maximum value of 0.1 (Barlaz, 2006)

0 0.02 0.04 0.06 0.08 0.1 0.12

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Infiltration Rate (m/yr)Gas Generation Rate (/yr)

Figure 3-4. Relationship between the infiltra tion rate and the gas generation rate.

3.1.2.7 Chemical-Specific Parameters Values for a series of chemical-specific parameters used by the landfill source model appear in Appendix B . Several of these parameters are basi c chemical characteristics such as Henry’s law constant, organic carbon distributio n coefficient, molar volume, molecular weight, boiling point, critical temperature, and critical pressure. Standard references such as the Superfund Chemical Data Matrix (SCDM) and the Hazardous Substances Data Bank (HSDB) provided values for these chemical char acteristics (U.S. EPA, 2002b; NLM, 2006). The LFCR model provides the capa bility of adjusting Henry’s constants and diffusivities to reflect their dependence on temperature. As described in Appendix B , the Fuller, Schettler, Giddings method was used to adjust diffusivities in air; the Hayduk and Laudie method was used

Do not cite or quote. 3-19Review Draft Section 3.0 Analysis
to adjust diffusivities in water; and the Clausius-Clapeyron met hod was used to adjust Henry’s constants (Fuller et al., 1966; Ly man, 1990; Perry and Green, 1997). Values for the permeation coefficient desc ribing geomembrane sorption and diffusion processes were derived either fr om laboratory values reported in the literature or from an empirical correlation presented in Sangam and Rowe (2001). This correlation relates the permeation coefficient to the octanol-water partition coefficient. Appendix B provides this correlation and further details about the permeation coefficient input values. Hydrolysis rates (abiotic degr adation rates) were taken fr om U.S. EPA (1993b) and NLM (2006). A uniform distribution ranging over two orde rs of magnitude was used to represent the variability and uncertainty. Howeve r, the influence of abiotic degradation was negligible relative to biodegradation, because the ab iotic degradation rates were or ders of magnitude lower than biodegradation rates. Values for biodegradation half -lives were collected from several sources, including Howard et al. (1991), Aronson and Howard ( 1997), and U.S. EPA (1999b). Because landfill waste primarily degrades under anoxic conditio ns, reported anaerobic degradation rates were used. Initially, ranges of half-life values were assembled to represent variable conditions. However, based on peer review comments and di scussions with EPA, the most conservative values within the ranges were selected for a ll simulations (the maximum half-lives/minimum biodegradation rates). It should be noted that the biodegradation ra te values generally were not developed for landfills. Rather, the secondary data sources reflect the broader environmental science literature. Therefore, significant uncertainty remains in the biodegradation rate estimates. Relatively little information was identified in the literature characterizing field-scale biodegradation rates in landfills. The analysis also did not consider the known correlation between moisture content and biological activit y. However, using the maximum available halflife value achieved EPA’s goal of greater conservatism in th e biodegradation estimates.

3.1.3 Source Model Outputs With two liner scenarios, 20 solvents, a nd 10,000 realizations per liner and solvent combination, the analysis included 400,000 LFCR source model simulations. Table 3-4 provides process summary results from the source m odeling simulations. The average percent mass balance error of 0.5 percent was maintained be low the target 1 percen t. A relatively small percentage of runs (15 percent) exceeded th is 1 percent target; however the maximum mass balance error was only 1.4 per cent for one simulation. The average simulation time was 0.05 minutes, with a total runtime for all 400,000 si mulations of 22,040 minutes , or approximately 15 days. The simulations were performed in parallel on multiple processors, so that the actual time to complete the simulations was a few days . Table 3-4 also provides average, maximum, and minimum landfill characteristics, including the number of lifts, columns, and daily cells, as well as the daily cell volume, he ight, and width. The number of columns and daily cell volume for the composite and no-liner scenarios differ sli ghtly due to the fact th at the liner occupies some of the landfill volume in the composite liner scenario.

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Table 3-4. Process Summary Results for All Solvents

Composite-Liner Scenario No-Liner Scenario

Result Avg Max Min Avg Max Min

Percent mass balance error (%) 0.5 1.4 4.3E-05 0.5 1.4 1.0E-05
Simulation runtime (minutes) 0.05 0.13 0.02 0.06 0.1 0.0166667

Number of lifts 6.4 92 2 6.5 92 2
Number of columns (in one landfill cell) 632 1,764 16 556 1,600 16
Number of daily cells (in one landfill cell) 1,454 1,825 1,095 1,454 1,825 1,095
Daily cell volume (m 3 ) 821 12,304 0.6 856 12,428 0.8
Daily cell/lift height (m) 3.3 6.5 1.0 3.3 6.5 1.0
Daily cell width (m) 13 98 0.6 13 98 0.6

In response to peer review comments on the LFCR model, we developed several types of analyses shown in Tables 3-5 through 3-7 to characterize the behavior of solvents in the landfill, focusing on key processes that control landfill mass losses. These analyses were necessary because the LFCR model was developed specifically fo r this analysis and represents a significant step forward in simulating landfill releases for a national-scale analysis. Each of the tables presents percent losses of solvent mass associated with specific processes for the no-liner and composite-liner scenarios, respectively, and s hows the percent loss associated with the 90th percentile risk, the minimum percent loss, and the maxi mum percent loss. Table 3-5 presents results for all primary fate and transport pathwa ys, including

 Diffusion of solvents from the bottom of the landfill

 Aqueous advection (leaching) of solvents with water leaving the bottom of the landfill (in the composite-liner scenario, this mass is recovered by a leachate collection system except for the fraction associated with liner l eakage; for the no-liner scenario, this mass reaches the unsaturated zone)

 Vapor advection of solvents with landfill gas flowing from the landfill into the air

 Diffusion of solvents from the landfill into the air (includes diffusion with and without cover)

 Degradation of aqueous phase solvents (e.g., biodegradation).

The percent contributions across these primary loss mechanisms and the mass balance error sum to 100 percent, indica ting that these processes accoun t for 100 percent of the solvent mass that is “lost” to the landfill. The first three rows of Table 3-5 present the averages across all solvents for the no-liner scenario, the composite-l iner scenario, and both scenarios combined. As shown in row three of this table, the largest aver age loss across both liner types is due to aqueous degradation (38 percent), followed by the advective losses to air (19 percent), diffusive losses to air (10 percent), diffusive losse s to the unsaturated zone (9 percent), and aqueous advective losses (7 percent). Relatively speaking, these pe rcent contributions are consistent with the simulation results from the MOCLA model, indica ting that (1) degradatio n can be a dominant

Do not cite or quote. 3-21Review Draft Section 3.0 Analysis

Do not cite or quote. 3-22 loss mechanism, and (2) advection of landfill gase s can be significant for volatile constituents (Kjeldsen et al., 2002). In addition, Table 3-5 also shows the percen t of solvent mass still remaining in the landfill at the end of the maximum landfill simu lation time (i.e., 200 years). Note that the percentage remaining is significant (greater than 60 percent at the 90th pe rcentile risk) for some of the more recalcitrant solvents, such as cr esol; 1,1,2-trichloroethane; and xylene. The mass loss rates to air and groundwater gene rally reach their maximum levels around the end of the landfill operating life, with the maximum fluxes to air and groundwater occurring on average around year 40 of the simulation. Because the mass loss ra tes at the end of the simulation are typically very low, extending the landfill simulation time beyond the 200 year maximum and thus decreasing the percent mass remaining would not re sult in a substantial change in the mass losses and flux estimates required by the groundwater and air pathway models.

Table 3-6 provides the total percenta ges represented by mass tran sfer to the atmosphere and to the unsaturated zone (inputs to the ground water and air transport models). The total mass loss to the atmosphere is the sum of contribu tions due to vapor diffusion and vapor advection. The total mass loss to groundwater is the sum of contributions from aqueous diffusion and leachate (for the no-liner scenario) or liner leakage (for the composite-liner scenario). Table 3-7 gives the diffusive no-cover mass loss that occurs while the daily waste is uncovered (a fraction of the total vapor diffusive losse s in Table 3-5), as well as line r leakage mass loss (a fraction of the aqueous advective mass loss in Table 3-5) . Mass loss during active waste addition (with no cover) was relatively less signi ficant, with an average mass loss of 3 percent (row three in Table 3-7). In selected cases, the no-cover ma ss loss was more significant (maximum percentage loss of 75 percent for a single run), but in only 3 percent of the cases was the no-cover mass loss percentage greater than 10 per cent. Although the rate of no-cove r loss is typically dominant for the active daily cell, th is daily cell accounts for a small volume of waste and a small time increment relative to the entire landfill. In the no-liner scenario, all aqueous advect ion losses reach the s ubsurface below the landfill. In the composite-liner scenario, aqueous advection losses are recovered in a leachate collection system, with the excep tion of the liner leakage. The results differ significantly among the different solvents , varying predictably with respect to differences in chemical properties. For example, the more volatile solvents (e.g., tetrachloroethylene, benzene) exhibit greater advective losses in the vapor phase. The least volatile solvents (e.g., ethyl ace tate, butanol) are primarily lo st through degradation, because they generally have greater residence time in the landfill and are thus more available for degradation. The mass loss to groundwater is cons istently greater in th e no-liner versus the composite-liner scenario, including the total loss to groundwater (Table 3-6) and diffusion to the unsaturated zone (Table 3-5).Review DraftSection 3.0 AnalysisTable 3-5. Percent Contributions of Primary Loss Mechanisms in the LandfillDiffusion toSubsurfaceAqueousAdvectionVaporAdvectionDiffusion toAtmosphereAqueous DegradationMass Remaining inLandfill at 200 YearsMass BalanceErrorLiner90thRange90thRange90thRange90thRange90thRange90thRange90thRangeNone15.290 - 74.1611.170 - 91.0518.170 - 96.19.020.01 - 80.9233.890.01 - 98.9211.980.1 - 86.790.480 - 1.43Composite2.280 - 26.442.320 - 87.5919.890 - 96.2311.630.01 - 95.4241.240.02 - 98.9422.190.1 - 88.960.450 - 1.43All solventsBoth8.790 - 74.166.740 - 91.0519.030 - 96.2310.320.01 - 95.4237.560.01 - 98.9417.090.1 - 88.960.460 - 1.43Solvent-Specific Percent ContributionsNone1.70.04 - 37.738.60 - 39.9710.10.85 - 21.934.10.44 - 43.3675.134.71 - 95.610.10.1 - 0.10.30.01 - 0.89AcetoneComposite0.020 - 0.079.80 - 48.614.90.9 - 21.923.20.44 - 48.7981.534.83 - 96.070.10.1 - 0.10.50.01 - 0.92None15.40.55 - 72.010.20 - 5.5958.12.48 - 94.1624.41.28 - 70.921.50.15 - 15.220.10.1 - 16.730.30 - 1.15BenzeneComposite1.40.08 - 15.860.80 - 8.0354.83.45 - 94.2337.51.2 - 95.423.90.26 - 16.591.60.1 - 20.560.030 - 1.08None0.70 - 7.426.90 - 20.950.20.06 - 0.540.70.03 - 3.7690.175.96 - 98.180.10.1 - 2.661.30.87 - 1.43ButanolComposite0.0050 - 0.210.020 - 18.440.10.06 - 0.550.10.03 - 3.9797.879.54 - 98.230.60.1 - 2.651.41.08 - 1.43None34.81.24 - 73.731.50 - 11.5843.53.25 - 70.848.70.79 - 634.50.31 - 18.196.40.1 - 54.120.60 - 1.2ChlorobenzeneComposite7.20.4 - 26.440.60 - 7.7132.37.07 - 70.9222.71.03 - 86.0511.10.84 - 19.1325.80.24 - 57.330.30 - 1.14None1.00.01 - 8.1827.90 - 91.050.10.03 - 0.210.10.01 - 1.9919.06.92 - 37.0351.80.1 - 84.420.20.02 - 1.05Cresols (total)Composite0.40.02 - 2.159.00 - 52.250.10.03 - 0.220.20.01 - 1.9526.115.19 - 38.5864.028.12 - 84.210.10.02 - 0.59None2.60.06 - 49.3780.00 - 82.511.50.53 - 2.871.60.06 - 13.2213.311.15 - 76.580.10.1 - 50.50.80.14 - 1.25CyclohexanoneComposite0.30.02 - 2.710.70 - 72.891.60.55 - 3.020.80.05 - 8.9452.620.96 - 80.6943.72.93 - 51.020.30.22 - 1.05None27.30.73 - 72.680.40 - 14.7419.23.78 - 35.7512.60.37 - 55.346.70.58 - 14.3533.60.1 - 78.340.30 - 1.13Dichloro-benzene, 1,2-Composite1.50.16 - 16.870.10 - 7.4120.27.67 - 35.863.10.44 - 62.688.92.92 - 15.2966.211.86 - 82.060.10 - 0.64None0.010 - 0.270.050 - 36.590.0050 - 0.030.10.01 - 0.8598.462.28 - 98.570.10.1 - 0.11.30.57 - 1.412-Ethoxy-ethanolComposite0.010 - 0.091.40 - 39.630.010 - 0.030.10.01 - 0.9497.259.37 - 98.580.10.1 - 0.11.30.69 - 1.41None9.40.02 - 27.733.10 - 10.482.20.49 - 5.561.30.11 - 14.9782.763.88 - 97.490.10.1 - 0.11.20.59 - 1.38Ethyl acetateComposite0.020 - 0.111.30 - 12.262.00.5 - 5.581.50.11 - 16.4693.873.06 - 97.650.10.1 - 0.11.20.79 - 1.39None32.81.16 - 74.131.10 - 5.834.03.7 - 57.977.90.63 - 61.881.70.09 - 4.0522.10.1 - 72.80.50 - 1.19Ethyl benzeneComposite14.20.34 - 23.720.60 - 3.1924.09.31 - 57.9520.30.8 - 79.922.20.34 - 4.3438.32.3 - 75.440.40 - 1.01None0.10 - 5.220.80 - 12.352.30.07 - 3.572.10.14 - 17.3394.478.49 - 98.920.10.1 - 0.10.30.04 - 0.58IsobutanolComposite0.020 - 0.113.20 - 15.690.50.06 - 3.571.00.14 - 19.1694.978.3 - 98.940.10.1 - 0.10.30 - 0.58(continued)Do not cite or quote.3-23Review DraftSection 3.0 AnalysisDo not cite or quote.3-24Table 3-5. (continued)Diffusion toUnsaturated ZoneAqueousAdvectionVaporAdvectionDiffusion toAtmosphereAqueous DegradationMass Remaining inLandfill at 200 YearsMass BalanceErrorLiner90thRange90thRange90thRange90thRange90thRange90thRange90thRangeSolvent-Specific Percent Contributions (continued)None5.20.12 - 61.577.60 - 89.480.80.36 - 2.040.50.08 - 18.9615.55.14 - 63.980.10.1 - 59.510.30.08 - 1.16MethanolComposite0.020 - 0.0414.40 - 87.591.60.38 - 2.232.60.07 - 11.6346.58.24 - 71.1634.51.51 - 60.190.40.13 - 0.93None19.00.26 - 63.831.50 - 5.4327.01.77 - 89.2641.21.78 - 80.9211.21.32 - 62.30.10.1 - 0.10.010 - 0.99MethylenechlorideComposite0.10.01 - 1.160.50 - 11.553.32.19 - 89.3928.21.79 - 94.7217.72.52 - 65.810.10.1 - 0.10.10 - 1.11None0.90.03 - 35.338.10 - 31.199.80.9 - 29.1512.10.51 - 48.1968.933.22 - 95.240.10.1 - 0.10.20 - 0.73Methyl ethylketoneComposite0.010 - 0.31.40 - 39.3610.00.92 - 29.141.70.51 - 53.786.439.62 - 95.90.10.1 - 0.10.40 - 0.75None3.40.02 - 25.823.50 - 10.056.30.64 - 18.724.60.31 - 33.6481.652.05 - 96.930.10.1 - 0.10.50.03 - 0.88Methyl isobutylketoneComposite0.0020 - 0.160.10 - 13.829.60.61 - 18.722.10.31 - 37.4887.658.85 - 97.360.10.1 - 0.10.40.01 - 0.89None18.01.34 - 74.160.10 - 7.4539.33.22 - 71.094.00.84 - 64.164.80.14 - 9.133.30.1 - 59.540.40 - 1.19TolueneComposite7.20.25 - 18.470.70 - 4.7532.67.63 - 71.2238.00.81 - 874.50.44 - 9.8316.50.6 - 63.490.40 - 1None44.00.45 - 71.651.90 - 3.0618.92.63 - 96.14.41.3 - 71.656.10.07 - 7.5724.10.1 - 15.610.70 - 1.141,1,2-Tri-chloroethaneComposite5.80.1 - 10.951.20 - 14.2311.46.07 - 24.917.20.32 - 52.87.24 - 15.4267.121.3 - 84.590.10 - 0.42None55.20.45 - 71.650.10 - 3.0612.42.63 - 96.131.91.3 - 71.650.20.07 - 7.570.10.1 - 15.610.10 - 1.14Trichloro-ethyleneComposite1.20.09 - 19.720.10 - 4.4487.73.52 - 96.234.11.31 - 95.232.90.12 - 8.323.30.1 - 18.370.50 - 1.1None21.80.5 - 71.910.010 - 2.1262.72.95 - 96.0115.01.24 - 69.780.10.01 - 1.030.30.1 - 23.550.10 - 1.16Tetrachloro-ethyleneComposite4.90.13 - 24.30.20 - 2.4938.24.11 - 96.0955.81.34 - 94.160.10.02 - 1.120.30.1 - 26.850.50 - 1.14None12.50.64 - 73.250.10 - 4.7215.03.68 - 28.293.00.33 - 57.382.30.16 - 3.2766.90.1 - 86.790.20 - 1.11Xylene (mixedisomers)Composite1.40.17 - 19.170.030 - 1.913.06.38 - 28.272.60.44 - 56.011.80.79 - 3.5181.120.74 - 88.960.040 - 0.5Notes:The percentages are the proportion of total mass added to the landfill for each simulation.90th refers to the 90th percentile risk simulationDiffusion to atmosphere includes diffusion with and without the cover.Degradation includes biodegradation and hydrolysis (hydrolysis was insignificant relative to biodegradation).Review Draft Section 3.0 Analysis

Table 3-6. Percent Mass Loss from the Landfil l to the Atmosphere and Groundwater

Total Loss to Atmosphere Total Loss to Groundwater
Liner 90th Range 90th Range

None 27.19 0.01 - 97.92 26.46 0 - 92.11 Composite 31.52 0.01 - 99.17 2.56 0 - 26.44 All Solvents

Both 29.36 0.01 - 99.17 14.51 0 - 92.11 Solvent-Specific Mass Losses

None 14.25 2.86 - 45.53 10.30 0.18 - 47.52 Acetone Composite 8.08 2.81 - 51.01 0.25 0 - 2.73

None 82.56 25.56 - 96.14 15.53 0.68 - 72.01 Benzene Composite 92.27 60.22 - 98.91 1.39 0.08 - 16.24

None 0.86 0.13 - 4 7.61 0.01 - 22.08 Butanol Composite 0.19 0.13 - 4.21 0.01 0 - 1.14

None 52.25 17.9 - 86.82 36.31 1.47 - 73.74 Chlorobenzene Composite 54.98 27.7 - 95.09 7.23 0.4 - 26.44

None 0.15 0.06 - 2.11 28.89 0.02 - 91.9 Cresols (total) Composite 0.33 0.06 - 2.08 0. 60 0.02 - 12.12

None 3.14 0.79 - 14.55 82.67 0.18 - 85.77 Cyclohexanone Composite 2.33 0.73 - 10.66 1.06 0.02 - 14.88

None 31.82 9.55 - 69.59 27.63 1.24 - 72.7 Dichlorobenzene, 1,2- Composite 23.28 10.02 - 76.29 1.52 0.16 - 16.87

None 0.09 0.01 - 0.86 0.06 0 - 36.68 2-Ethoxyethanol Composite 0.12 0.01 - 0.96 0.03 0 - 2.01

None 3.55 0.88 - 16.66 12.50 0.09 - 27.73 Ethyl acetate Composite 3.51 0.86 - 18.23 0.09 0 - 0.58

None 41.90 15.06 - 85.18 33.87 1.48 - 74.14 Ethyl benzene Composite 44.32 20.23 - 92.81 14.36 0.34 - 23.76

None 4.36 0.49 - 17.66 0.90 0.01 - 13.22 Isobutanol Composite 1.44 0.48 - 19.45 0.08 0 - 0.61

None 1.27 0.69 - 19.89 82.77 0.3 - 92.11 Methanol Composite 4.13 0.69 - 13 3.51 0 - 22.4

None 68.23 21.83 - 92.68 20.45 0.4 - 63.85 Methylene chloride Composite 81.50 32.95 - 98.16 0.05 0.01 - 1.53

None 21.88 3.27 - 50.47 8.98 0.16 - 39.39 Methyl ethyl ketone Composite 11.70 3.2 - 55.99 0.04 0 - 2.14

None 10.91 1.77 - 35.8 6. 91 0.08 - 25.82 Methyl isobutyl ketone Composite 11.71 1.73 - 39.56 0.01 0 - 0.6

None 43.38 18.5 - 88.76 18.15 1.44 - 74.16 Toluene Composite 70.58 27.97 - 96.5 7.41 0.25 - 18.47 (continued)

Do not cite or quote. 3-25Review Draft Section 3.0 Analysis

Table 3-6. (continued)

Total Loss to Atmosphere Total Loss to Groundwater
Liner 90th Range 90th Range Solvent-Specific Mass Losses (continued)

None 23.28 7.6 - 62.83 45. 88 1.36 - 75.38 1,1,2-Trichloroethane Composite 18.59 7.56 - 63.42 6.00 0.1 - 10.95

None 44.28 26.91 - 97.92 55.34 0.5 - 71.65 Trichloroethylene Composite 91.84 68.29 - 99.17 1.26 0.09 - 19.9

None 77.63 27.05 - 97.81 21.82 0.54 - 71.91 Tetrachloroethylene Composite 94.05 66.52 - 99.02 4.91 0.13 - 24.38

None 17.99 8.01 - 69.12 12.63 0.8 - 73.25 Xylene (mixed isomers) Composite 15.55 8.51 - 69.86 1.42 0.17 - 19.17

Notes: The percentages are calculated as the proportion of total mass added to the landfill for each simulation. 90th refers to the 90th percentile risk simulation.

Table 3-7. Percent Mass Loss from the Landfil l Due to No-Cover Diffusion and Liner Leakage

Mass Loss with No Cover Liner Leakage
Liner 90th Range 90th Range

None 2.89 0.01 - 71.33 11.17 Composite 2.26 0.01 - 75.19 0.28 0 - 22.38 All Solvents

Both 2.57 0.01 - 75.19 5.72 0 - 22.38 Solvent-Specific Mass Losses

None 3.68 0.31 - 35.87 8.64 Acetone Composite 1.85 0.31 - 39.26 0.23 0 - 2.69

None 6.70 0.55 - 52.39 0.16 Benzene Composite 15.14 0.55 - 56.44 0.02 0 - 0.87

None 0.31 0.02 - 3.23 6.86 Butanol Composite 0.09 0.02 - 3.6 0.00 0 - 0.99

None 2.54 0.28 - 32.53 1.54 Chlorobenzene Composite 1.95 0.28 - 35.69 0.02 0 - 0.89

None 0.07 0.01 - 1.6 27.85 Cresols (total) Composite 0.16 0.01 - 1.79 0.22 0 - 10.52

None 0.36 0.05 - 6.52 80.03 Cyclohexanone Composite 0.73 0.05 - 7.28 0.74 0 - 13.1

None 0.99 0.15 - 19.68 0.36 Dichlorobenzene, 1,2- Composite 1.48 0.15 - 21.8 0.05 0 - 0.96

None 0.08 0.01 - 0.82 0.05 2-Ethoxyethanol Composite 0.10 0.01 - 0.92 0.02 0 - 1.95 (continued)

Do not cite or quote. 3-26Review Draft Section 3.0 Analysis

Table 3-7. (continued)

Mass Loss with No Cover Liner Leakage
Liner 90th Range 90th Range Solvent-Specific Mass Losses (continued)

None 0.83 0.09 - 11.47 3.07 Ethyl acetate Composite 1.32 0.09 - 12.77 0.07 0 - 0.5

None 3.71 0.22 - 26.62 1.11 Ethyl benzene Composite 5.01 0.22 - 29.33 0.13 0 - 0.4

None 2.03 0.12 - 16.25 0.80 Isobutanol Composite 0.49 0.12 - 18.03 0.06 0 - 0.53

None 0.39 0.06 - 7.71 77.57 Methanol Composite 0.88 0.06 - 8.6 3.49 0 - 22.38

None 11.71 0.97 - 71.33 1.46 Methylene chloride Composite 4.59 0.95 - 75.19 0.00 0 - 0.76

None 2.28 0.37 - 41.06 8.10 Methyl ethyl ketone Composite 1.49 0.36 - 44.74 0.03 0 - 1.93

None 4.24 0.24 - 28.33 3.52 Methyl isobutyl ketone Composite 0.62 0.23 - 31.18 0.00 0 - 0.49

None 1.69 0.29 - 32.61 0.15 Toluene Composite 2.14 0.28 - 35.77 0.18 0 - 0.65

None 1.38 0.14 - 17.25 1.90 1,1,2-Trichloroethane Composite 0.94 0.14 - 19.13 0.22 0 - 2.07

None 6.30 0.58 - 54.29 0.11 Trichloroethylene Composite 1.51 0.57 - 58.37 0.01 0 - 0.43

None 6.62 0.52 - 50.99 0.01 Tetrachloroethylene Composite 3.55 0.51 - 55.02 0.00 0 - 0.23

None 1.89 0.14 - 17.34 0.10 Xylene (mixed isomers) Composite 1.09 0.13 - 19.23 0.01 0 - 0.25

Notes: The percentages are calculated as the proportion of total mass added to the landfill for each simulation. 90th refers to the 90th percentile risk simulation. Mass loss with no cover is a component of diffusion to atmosphere in Table 3-5. Liner leakage is a component of aqueous advection in Table 3-5.

As seen in Tables 3-5 and 3-7, the mass lo ss due to liner diffusion exceeds liner leakage for all but four solvents (methanol, acetone, 2-ethoxyethanol, and methyl ethyl ketone). Generally, constituents that more readily sorb to the geomembrane surface exhibit greater diffusive flux through the geomembrane due to th e resulting increased concentration gradient across the geomembrane. This pattern is reflect ed in the correlation used to estimate the geomembrane permeation coefficient (see Appendix B , equation B-8), whereby the permeation coefficient increases with the octanol-water distribution coefficient (Sangham and Rowe, 2001). Diffusive transport through the liner exceeds leak age by up to three orders of magnitude. Several researchers have documented the importanc e of diffusion through geomembranes and emphasized that this transport mechanism can be much more significant than liner leakage for many volatile organic compounds ( VOCs) (Park et al. 1996; Edil, 2003; Foose 2002). Park et al. (1996) demonstrated that the diffusive mass flux through a geomembrane can be more than two

Do not cite or quote. 3-27Review Draft Section 3.0 Analysis

orders of magnitude greater than the mass flux due to the typical number and size of holes found in landfill geomembranes. The analysis in Foos e (2002) demonstrated th at diffusive fluxes for many VOCs can be four to six or ders of magnitude greater than the leakage flux. Therefore, the analysis result that diffusion through the geomem brane can exceed leakage by up to three orders of magnitude is consiste nt with the literature.


### 3.2 Air Dispersion Modeling
This section describes the methodology and the model that was used to predict the fate and transport of chemical solvents in ambi ent air after their release from a landfill. Section 3.2.1 provides an overview of the conceptual model, including the basic approach and assumptions. Section 3.2.2 discusses the input parameters and va lues used in this risk analysis. Section 3.2.3 discusses the model outputs. Appendices G and H , listed below, provide additional information about the dispersion modeling ou tput and how they were applied in this risk analysis:

 Appendix G , Unitized Air Concentrations (UACs) , describes the linear interpolation methodology used in determining the UAC values.

 Appendix H , Equations, presents the equations that were used to estimate ambient air exposure concentrations using the UAC values.

3.2.1 Conceptual Model Air dispersion modeling uses a computer-based set of calculations to estimate ambient ground-level constituent concentrations associ ated with solvent releases from a waste management unit (WMU). The dispersion model uses information on meteorology (e.g., wind speed, wind direction, temperature) to estim ate the movement of solvents through the atmosphere. Movement downwind is largely determined by wind speed and wind direction. Dispersion around the centerline of the contaminant plume is estimated using empirically derived dispersion coefficients that account for horiz ontal and vertical movement of solvents.

3.2.1.1 Overview In this analysis, modeling was performed to estimate ambient air concentrations for volatile solvents released from landfills. Modeling was performed using EPA’s ISCST3 to estimate total air concentrati ons at various locations surr ounding the landfills. The air concentrations developed by ISCST3 were based on a unit emission rate of 1 μg/s-m 2 and are called unitized air concen trations (UACs) (μg/m 3 per unit emission rate of 1 μg/s-m 2 ). As shown in Appendix H , the exposure model multiplies the UACs by the constituent-specific emission rates (predicted by the landfill s ource model) and appropriate c onversion factors to develop the final constituent-specific ambient air estimates (mg/m 3 ). This analysis was conducted using a conceptual site model that coul d exist anywhere in the contiguous 48 states. To capture the national variation in environmental settings, a regionally based approach was applied. As a result, m odeling was conducted usi ng surface and upper air data from 31 different meteorological regions. To capture the range of the municipal landfill size distribution, 19 representative surf ace areas were modeled. Outputs were generated for receptor points defined by a polar gr id that simulated locating individuals in any of 36 directions at any of 8 distances. Receptors were placed every 10° around the full 360° surrounding the landfill on each distance ring. The ring distances modeled in this analysis ranged from 25 to 2,000 m from the edge of the unit.

Do not cite or quote. 3-28Review Draft Section 3.0 Analysis

The resulting UACs were indexed based on si te location, landfill surface area, receptor distance, and receptor direction. These data were imported into a database that was used to construct a subset of 10,000 scenar io records for use in the Mont e Carlo analysis. To construct this data set, it was necessary to define the s ite location, landfill surface area, receptor distance, and receptor direction for each scenario record. The approach used to define these characteristics was as follows:

 Site location. The EPACMTP Climate Center locatio ns were used to place landfill locations, as described in Section 2.2.4 . For dispersion modeling, these locations were cross-referenced to one of the 57 IWAIR meteorological regions. As a result, 31 different meteorological regions were identified a nd assigned to each of the 10,000 records.

 Surface Area. The record-specific surface areas we re set to the same landfill surface areas used as input to the LFCR model.

 Receptor Distance. As described in Section 3.2.2 , the receptor distances were randomly selected.

 Receptor Direction. Each of the 10,000 records was asso ciated with a single direction that was randomly selected from one of the 36 directions.

Based on these record-specific indices, an interpolation appli cation selected an appropriate value from the UAC data set. In the event that the scenario-specific surface area fell between two of the representative sizes, an inte rpolation method estimated a UAC in relation to the modeled landfill sizes. Th e interpolation method is consistent with the methodology employed in the IWAIR model (U.S. EPA, 2002a ). The UAC methodology is further discussed in Appendix G

. 3.2.1.2 Key Assumptions This section presents key assumptions associ ated with the air modeling approach. More comprehensive documentation of the ISCST3 model and associated assumptions are available in the User’s Guide for the Industrial Sour ce Complex (ISC3) Dispersion Model (U.S. EPA, 1995a).

 The distance and direction between the landf ill source and the receptor was assumed to be random.

 The model does not include photochemical reacti ons or degradation of constituents in the air, which results in additional model uncertainty for some solvents.

 ISCST3 is assumed to provide an accurate approximation of the transport of volatile solvents from landfill sources in the atmosphere.

 The model assumes average meteorological c onditions (5-year aver ages). Thus, it is assumed that transient conditi ons are insignificant in ap proximating long-term average risk for this analysis.

3.2.2 Model Inputs For the Monte Carlo simulations, each of the 10,000 scenario records was associated with UACs that were indexed based on site locati on, landfill surface area, receptor distance, and receptor direction. Each of thes e indices and other inputs to ISCST3 are discussed below.

Do not cite or quote. 3-29Review Draft Section 3.0 Analysis

Site Location. EPACMTP Climate Center locations were used to place landfill locations, as described in Section 2.2.4 . Each location was then cross-referenced to one of the 57 meteorological regions developed for the IWAIR model. As a result, 31 different meteorological regions were identified. For each landfill lo cation, modeling was conducted using 5 years of existing surface and upper air meteorological data that were preprocessed and used in the development of IWAIR.

Landfill Surface Area. As discussed in Section 3.1.2.4 , landfill surface areas modeled in the analysis were based on the EPA-WBJ data. Because each of the 10,000 simulations was associated with a unique surface area, it was not feasible to perform dispersion modeling for all su rface areas considered in the analysis. Instead, dispersion modeling was performed for representative surface areas and a linear interpolation was performed, as discussed further below. For this analysis, ISCST3 modeling was conducted for landfills with surface areas that are representative of the municipal landfill distribution discussed in Section 3.1.2.4 (i.e., ranging from 4,000

m 2 to approximately 17,000,000 m 2 ). To represent this range, 19 surface areas were identified for modeli ng in the current analysis (see text box). Landfill Areas Modeled
4,000 m 2 420,000 m 2

6,045 m 2 607,000 m 2
8,090 m 2 906,528 m 2

14,145 m 2 1,157,442 m 2

20,200 m 2 1,408,356 m 2
40,450 m 2 3,146,678 m 2

60,700 m 2 4,885,000 m 2

127,350 m 2 9,350,000 m 2
194,000 m 2 17,000,000 m 2

30 7 0 00 m 2

To ensure that the areas de scribed above were appropriate for linear interpolation, an analysis was performed that was similar to one implemented as part of the development of IWAIR. The goal of this analysis was to evaluate the adequacy of the selected areas in achieving a goal of less than 5 percent in terpolation error. Under the current effort, interpolation errors were computed using ISCST3 outputs generated for the surface areas descri bed above, as well as for areas representing the midpoints between each size. Modeling was performed for the representative surface areas using meteorological data for three locations included in IWAIR: Fresno, CA; Minneapolis, MN; and Salt Lake City, UT. These stations represent a range of wind roses and climate regimes. 15 Results from the analysis indicated that the goal of less than 5 percent interpolation error was achieved. As a c onsequence, the 19 surface areas described above were used for the current analysis. Receptor Distance. Eight ring distances were used in this analysis (s ee text box). These ring distances represent the range of distances incl uded in prior analyses and models, such as the 3MRA modeling system. The specifi c distances were selected to ensure complete coverage in the air estimates, particularly near the source of the emissions. As input to the Monte Carl o analysis, the 10,000 scenario distances were rando mly selected from these ring distances. Ring Distances

25 m 300 m

50 m 500 m

75 m 1,000 m Receptor Direction. ISCST3 modeling was performed using a polar grid that simulated locating receptors in any of 36 directions (i.e., on each distance ring, recepto rs were placed every 10° around 150 m 2,000 m

15 For a given location, a wind rose shows the frequency of wind directions about a 16-point compass, and uses different shading to represent wind speeds (U.S. EPA, 2006b).

Do not cite or quote. 3-30Review Draft Section 3.0 Analysis

the full 360° surrounding the landfill). In the Monte Carlo analysis, each of the 10,000 records was associated with a single direction that was randomly selected from one of these 36 directions. Source Shape. Landfills were modeled as ground-level sources using a 20-sided polygon shaped to approximate a circle. This option wa s chosen because there are no actual data on the shape of sources, and a circular source will minimize air modeling errors caused by source shapes and orientation.

Rural vs. Urban. Similar to the IWAIR modeling, the ru ral setting was used to reflect typical landfill locations (i.e., landfills are typically not located in large cities).

3.2.3 Model Outputs The ISCST3 outputs were processed and imported into a database that served as the basis for determining the scenario-specific UACs [(μg/m3) per (unit emission rate of 1 g/s-m2)] that were used as input for the exposure and risk mo del. The resulting databa se includes UACs that represent all modeled locations, 19 representative surface areas, 8 distances, and 36 directions. These data were used by an interpolation app lication to determine a UAC for each of the 10,000 scenarios. The resulting data set was then impor ted into the exposure and risk model database and used with volatile emission rates (g/s-m 2 ) calculated by the source models to determine ambient air concentrations. For each run ID, ai r concentrations of vol atile solvents were calculated for each year of emissions. Based on this time series of concentrations, the maximum, or peak, concentration was identified. The tim ing of the maximum ambient air concentration represented the midpoint of exposure. Thus, th e time period of exposure was centered on the peak (or maximum) ambient air concentration. Th e average air concentrat ion across this time period was used to estimate inhalation exposures. Appendix H presents the equations that were used to estimate ambient air exposures.


### 3.3 Groundwater Modeling
This section describes the methodology and the m odels that were used to predict the fate and transport of solvents from a landfill to underlying soils and gr oundwater to determine impacts on drinking water wells that are connected to groundwater. Section 3.3.1 provides an overview of the conceptual model, incl uding the basic approach and assumptions. Section 3.3.2 discusses the input parameters and va lues used in this risk analysis. Section 3.3.3 discusses the model outputs. Appendices F and H provide additional information about the groundwater modeling and how it was used in the exposure model:

 Appendix F , Groundwater Modeling Parameters, presents the input values and the distributions used in modeling the groundwater pathway using EPACMTP.

 Appendix H , Equations, presents the equations th at were used to estimate groundwater
exposures.
3.3.1 Conceptual Model The groundwater pathway was modeled for th is analysis to estimate the receptor concentrations that result from a predicted rel ease of solvents from la ndfills. The release of a solvent occurs when liquid that percolates th rough the landfill generates leachate, which can infiltrate from the bottom of th e landfill into the subsurface.

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The waste solvents dissolved in the leachate are transported via aqueous-phase migration through the unsaturated zone (the soil layer under the landfill) to the underlying saturated zone (i.e., groundwater), and then downgradient to a hypothetic al receptor. For this analysis, the exposure concentration was evaluated as th e intake point of a hypothetical re sidential drinking water well (hereafter referred to as the “receptor well”). Figure 3-5 shows the conceptual model of the groundwater fate and the transport of contaminant releases from a la ndfill to a downgradient receptor well with associated dilution and atte nuation. Details about the modeled receptor well are provided in Section 3.3.1.1

. Appendix H presents the equations th at were used to estimate ingestion exposures resulting from th e consumption of drinking water.

Figure 3-5. Schematic diagram of gr oundwater modeling scenario.

3.3.1.1 Overview The transport of leachate from the landfill thr ough the unsaturated and saturated zones is evaluated quantitatively using EPACMTP (U .S. EPA, 2003a,c,d; 1997b). EPACMTP simulates the flow and transport of contaminants in th e unsaturated zone and aquifer beneath a waste disposal unit to yield the concentration that arrives at a specified receptor location. The LFCR source model described in Section 3.1 determines the leachate concentration used as input to EPACMTP. New functionality was added to the EPACMTP model, as described in the addendum to the EPACMTP Technical Background Document (U.S. EPA, 2003d), to create a dynamic, mass-conserving linkage between the source models and EPACMTP. The groundwater model accounts for advec tion, hydrodynamic disper sion, equilibrium linear or nonlinear sorp tion, and transformation processes vi a chemical hydrolysis. In this analysis, data were compiled from the scientific literature to devel op organic carbon partition coefficients to simulate equilibrium linear partitioning for organic constituents. EPACMTP is a composite model that consists of two coupled modul es: (1) a 1-D module that simulates infiltration and dissolved contaminant transport through the unsaturated zone, and (2) a saturated zone flow and transport module that includes three ground water transport solution options: (i) fully 3-D transport, (ii) quasi-3-D transport (a combination of cross-sectional and areal solutions), and (iii) pseudo-3-D transport (hybrid analytical and numerical solution). The applicability and appropriateness of each of the transport solu tion options depend on the problem

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considered. The pseudo-3-D solution is the most computationally efficient of the available options, and allows more Monte Carlo realizations to be included in th e modeling analysis. In addition, the pseudo-3-D solution can accurately and efficiently gene rate full breakthrough curves at the receptor location. For these reasons , the pseudo-3-D solution option was chosen for this analysis. The EPACMTP model algorithms are based on the assumption that the soil and aquifer are uniform porous media, and that flow and transport are described by Darcy’s law and the advection-dispersion equation, respectively. EPACMTP does not account for preferential pathways, such as fractures and macropores, or facilitated transport, which may affect the migration of strongly sorbing constituents. The primary transport mechanisms in the subsurface are the downward migration of dissolved solvents within infiltrating water in the unsaturated zone, a nd lateral movement of solvents along with the ambient groundwater flow in the saturated zone. The advective movement in the unsaturated zone is 1-D, wher eas the saturated zone module accounts for 3-D flow and transport. The model also considers mixing due to hydrodynamic di spersion in both the unsaturated and saturated zones. In the unsaturated zone, flow is gravity driven and prevails in the vertically downward direction. Therefore, the flow is modeled in the unsaturated zone as 1-D in the vertical direction. It is also assumed that transverse dispersion (both mechanical dispersion and molecular diffusion) is negligible in the un saturated zone. This assumption is based on the fact that lateral migration due to transverse disp ersion is negligible comp ared with the horizontal dimensions of the WMUs. In addition, this assu mption is environmentally protective because it allows the leading front of the constituent plume to arrive at the water table with greater peak concentration in the case of a finite source. In the saturated zone, the movement of constituents is primarily driven by ambient groundwater flow, which in turn is controlled by a regional hydraulic gradient and hydraulic conductivity in the aquifer formation. The model ta kes into account the ef fects of infiltration from the waste source, as well as regional recharge into the aquifer. The effect of infiltration from the waste source is an incr ease in groundwater flow in the hor izontal transverse and vertical directions underneath and in th e immediate vicinity of the wa ste source, as may result from groundwater mounding. This 3-D flow pattern will e nhance the horizontal a nd vertical spreading of the plume. Regional recharge outside of the waste source causes a (vertically) downward movement of the plume as it tr avels in the (longitudinally) downgradient groundwater flow direction. In addition to adv ective movement and groundwater flow, the model simulates the mixing of contaminants with groundwater due to hydrodynamic dispersion, which acts in the longitudinal direction (i.e., along the groundwater flow direction), as well as in horizontal and vertical transverse directions. Leachate constituents can be subject to comp lex geochemical interactions in soil and groundwater, which can strongly affect their rate of transport in the subsurface. EPACMTP treats these interactions as equilibrium sorption processes. The equilibrium assumption means that the sorption process occurs instantaneously or at le ast very quickly relative to the time scale of constituent transport. Although sorption, or the att achment of leachate constituents to solid soil or aquifer particles, may result from multiple chemical processes, EPACMTP combines these processes into an effective soil -water partition coefficient ( K

d

). The retardation factor, R
, accounts for the effects of equilibrium sorption of dissolved constituents onto the solid phase. R
, a function of the constituent-specific K d and the soil or aquifer prope rties, is calculated as follows:

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φ ρ

db

K R += 1 (Equation 3-1)

where

R = retardation factor (unitless)
ρ b = soil or aquifer bulk density (g/cm 3 ) K

d = solid-water partition coefficient (cm 3 /g) φ

= water content (in unsaturated zone) or porosity (in saturate d zone) (unitless).

As modeled in EPACMTP, the K d of an organic constituent is assumed to be constant and is calculated as the product of the mass fraction of organic car bon in the soil or aquifer and a constituent-specific organic carbon partition coefficient ( K

oc ). As can be seen in Equation 3-1, constituents with a low K d (or K oc ) value will have an R close to 1.0, which indicates that they will move at nearly the same velocity as the groundwater. Constituents with high K d values, such as certain semivol atile organic constituents and many metals, will have high R values and may move many times mo re slowly than the groundwater.

The concentration of constituents in both th e unsaturated zone and the saturated zone may be further attenuated by bioc hemical transformation reactions . EPACMTP can, in principle, model a wide range of transformati on processes, as long as they can be combined as a first-order biodegradation process or a first-order chemical degradation process. In practice, a more conservative approach was adopted in which onl y the degradation of organic constituents via hydrolysis transformation processes was simula ted. For those constituents that may be transformed via hydrolysis into toxic daught er products, EPACMTP can account for the formation and subsequent fate and transport of the daughter products. Hydrolysis rates were calculat ed from chemical-specific hyd rolysis rate constants using soil and aquifer temperature and pH values. Wh ereas chemical hydrolysis can be accurately modeled as a function of these factors, many othe r types of transformation processes, such as biodegradation, are much more site specific a nd can be highly variable —and therefore, much more difficult to simulate using a generic model such as EPACMTP. Thus, for this analysis, it was conservatively assumed that these processes do not occur; that is, biodegradation was not included in the subsurface environment beyond the landfill. The EPACMTP model simulates steady-state fl ow in both the unsaturated and saturated zones, and can accommodate either steady-state or transient contaminant transport. Steady-state transport modeling is a very protective modeling approach in which a WMU continues to release contaminants indefinitely (continuous source); eventually, the model will predict that the receptor well concentration reaches a constant value. However, in this analysis, transient transport simulations were performed. As discussed in Section 2.2.3 , this finite source approach simulates the amount of time over which the land fill operates and the time-dependent movement of chemical constituents in th e subsurface to the receptor well.

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Groundwater Receptor One of the most important inputs for EPACMT P is receptor location, which for this risk analysis included residential drinking water wells. EPACMT P can evaluate the exposure concentration at the intake point of a hypotheti cal residential drinking water well located at a specified distance from the downgradient edge of the WMU and at a specified depth below the water table. The location of the receptor well inta ke point is ultimately defined in terms of its x , y , and z coordinates relative to the center of the WMU’s downgradient edge (see Figure 3-6 [b], in which x rw refers to the downgradient distance from the edge of the WMU, y rw refers to the horizontal transverse distance from the plume centerline, and z rw (not shown in the figure) refers to the vertical distance below the water table). The planar coordinates ( x rw and y rw ) may be explicitly specified or they may be derived from a radial distance ( R rw ) and angle of rotation ( θ rw ) off the plume centerline, as show n in Figure 3-6 (a). The shaded areas in Figure 3-6 (a and b) conceptually represent the horizontal extent of the contaminant plume.

Figure 3-6. Schematic plan view showing procedure for determining the downstream location of the receptor well: (a) well loca tion determined using radial distance, R rw , and angle off center, θ rw ; and (b) well location generated randomly within plume limit.

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In this analysis, the x rw and y rw distances are derived from radial distance R rw and angle θ

rw and are defined as follows:

x rw = R rw cos( θ rw )
y rw = R rw sin( θ rw ) or the distance from the plume cente rline to the plume edge, whichever is smaller.

The z rw distance is defined as follows:

z rw = 0 to 10 m (or to the base of the aquifer if the aquifer thickness is less than 10 m) measured downward from the water table.

The well was placed at a downgradient distan ce of up to 1 mile, based on a nationwide distribution of the nearest downgradient resident ial wells from a survey of Subtitle D MSWLFs (U.S. EPA, 1993a). This distri bution was used to represent R rw and is presented in Appendix F

. It was assumed that the same distribution holds for other types of landfills as well. A uniform distribution within th e range of 0° to 90° was used to model θ rw . The limits on y rw locate the receptor well within the lateral extent of the plume (because of symmetry around the plume centerline, only half of the lateral extent of the plume is actually modeled). The distance from the plume centerline to the la teral edge of the plume, which is used to constrain y rw , is approximated as follows: 

# ( )

rw T xAREA AREA Edge Plume + ∗+ = 23

5.0 α (Equation 3-2)

where

AREA = area of an assumed square WMU (m 2 )

α T = horizontal transverse aquifer dispersivity (m)

x rw = downgradient distance of receptor we ll from the edge of the WMU (m).

The first term on the right side of Equati on 3-2 represents the contribution from the WMU area itself to the extent of the plume. The factor 0.5 represents the fact that the plume edge is calculated from the plume centerline (i.e., only half of the actual plume width is considered). The second term on the right side represents the la teral spreading of the pl ume due to transverse dispersion, with increasing downgrad ient distance from the WMU. The magnitude of dispersive effects is expressed in the α T parameter. The AREA and x rw terms represent the total downgradient plume travel distance, starting from the upgradient edge of the landfill. The approximation used above for the lateral ex tent of the contaminant plume is based on the assumption that the spreading of the plume in the horizontal transverse direction is caused by dispersive mixing, which results in a Gaussian profile of the plume cross-section. Under this assumption, this approximation of the location of the plume boundary will capture 99.7 percent of the contaminant mass. However, this approx imation does not take into account advective spreading of the plume due to lateral groundwater flow in the vicinity of the WMU and may therefore underestimate the actual lateral extent of the plume. However, in this case, the approximation was conservative, as it prevented placement of the we ll at the outer fringes of the plume, which would have a lower concentration comp ared with that of a well closer to the plume centerline.

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When EPACMTP was originally developed, th e angle of the receptor well off of the plume centerline was typical ly generated randomly, and x rw and y rw were derived from R rw and θ

rw . However, since that time, an improvement has been made to the modeling methodology that seeks to reduce the potential bias in observed concentrations a ssociated with large WMU areas when the observation location is de rived relative to the center of the downgradient edge of the WMU. This improved methodology, detailed in the EPACMTP Technical Background Document (U.S. EPA, 2003c), was used in the current analysis and allows the point of origin for the receptor location to move to the corner of the idealized WMU when the size of the WMU is large relative to R rw . Regardless of the point of origin, the constraint on y rw relative to the edge of the plume still applies. For this analysis, the depth of the well was varied uniformly throughout the aquifer thickness or throughout the upper 10 m of the aquifer thickness, whichever was less. That is, the well depth was never allowed to exceed 10 m below the water table. This limitation for well depth has been used in previous analyses primar ily for two reasons: (1) to be consistent with a residential well scenario (these wells are generall y shallow because of the higher cost of drilling a deeper well) and (2) to produce a conservative es timate of risk (because th e infiltration rate is generally lower than the groundwater seepage velocity, groundwater plumes tend to be relatively shallow).

Groundwater Pathway Simulations The leachate fluxes (g/m 2 /yr) estimated by the landfill source model were used as input to the groundwater fate and transport model, EPACMTP, to generate constituent concentrations at receptor wells and associated groundwater dilution and attenuation factor s (DAFs). A peak DAF is the ratio of the leachate concentration to the maximum 1-year observed receptor well concentration. Likewise, an N-Year Average DAF is the ratio of the leachate concentration to the maximum N-year observed receptor well concen tration (both definitions are based on an assumed constant initial leachate concentratio n). In other words, the DAF represents the reduction in constituent concentra tion that occurs before the leac hate reaches the well. The DAF is a dimensionless quantity, the value of which can vary from one to infinity. A DAF value of 1 corresponds to a relative receptor well concentrat ion of 1. This situation means that the exposure concentration at the receptor well is the same as the leachate concentration that enters the subsurface from the modeled WMU, and no diluti on or attenuation occurs along the subsurface pathway. Conversely, if the contaminant plume does not reach the receptor well at all, the receptor well concentration will be zero and the corresponding DAF will approach infinity. For the organic solvents considered in this analysis, the fate and transport equations in groundwater model are linear (i.e., the model pr oduces a linear isotherm); therefore, the predicted groundwater well concentration is linea rly proportional to the value of the leachate concentration. That is, if we doubled the solv ent concentration in leachate as input to EPACMTP, the model would predict a maximum con centration at the groundwater well that was double the original value, provided all other mode l parameters remain the same. The practical implication of this linearity is that once the DAF s have been calculated, they can be used to estimate the groundwater well concen tration at specific percentiles (e.g., 90th percentile) for any leachate concentration.

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For the groundwater pathway simulations in th is analysis, the flux of solvent from the landfill was not constant. Instead, the source mode l predicts a time series of leachate flux, whereby the mass transfer to the groundwater pathwa y varies from year to year. In this case, the degree of dilution and attenuation was calculated using the maximum calculated leachate flux for each run, or an effective leachate flux/concen tration. Hence, the DAF was considered an effective DAF, or DAF Eff , and the same properties of the DAF hold true for effective DAF. Therefore, constituents with similar fate and tr ansport properties (including volatility) would be expected to have similar effective DAFs. Conceptually, each source model leachate pr ofile processed by EPACMTP represents one possible outcome, and each realization has an equal probability of occurrence. The part of the Monte Carlo simulation performed with EP ACMTP (10,000 flow and transport realizations) resulted in a distribution of predicted receptor well concentrations and their corresponding effective DAFs; these results can be summari zed by constructing a CDF and selecting any percentile of interest. For example, the 10t h percentile effective DAF is the DAF that corresponds to the lowest 10 percen t of all the cases simulated, and is referred to as the DAF Eff 10
. Tables 3-8 and 3-9 provide a summary, by solvent and liner scenario, of the DAF Eff 10 and the DAF Eff 50 corresponding to the peak 1-year recepto r well concentration and two maximum average concentrations using av eraging periods corresponding to exposure periods for a child (7 years) and adult receptor (30 years). The DAF Eff 10 and the DAF Eff 50 are the DAFs that were used to calculate the 90th percentile and the 50th pe rcentile groundwater concen trations, respectively. The median groundwater retardat ion coefficients and the me dian biochemical hydrolysis degradation rates (lambda, yr -1 ) are also presented by solvent. Fo r a given liner scenario, solvents with similar values of retardation and hydrolysis have similar values for the DAF Eff 10
.

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Table 3-8. Groundwater Pathway DAF Eff 10

Solvent CAS No. Retardation a Hydrolysis a [yr -1 ] Liner Peak
DAF Eff 10 Child
DAF Eff 10 Adult
DAF Eff 10
NL 1.95 1.95 2 Methanol 67-56-1 1 0 GM 891 904 923

NL 2.13 2.16 2.27 Ethoxyethanol, 2- (ethylene glycol monoethyl ether) 110-80-5 1.001 0 GM 2430 2510 2550

NL 2.82 2.93 3.15 Methyl ethyl ketone 78-93-3 1.003 0 GM 8830 9010 9390

NL 3.37 3.51 3.79 Isobutyl alcohol 78-83-1 1.009 0 GM 8840 8880 8950

NL 2.06 2.08 2.18 Butanol, n- (n-butyl alcohol) 71-36-3 1.01 0 GM 1270 1280 1280

NL 2.76 2.83 3.02 Methyl isobutyl ketone 108-10-1 1.023 0 GM 8230 8430 8640

NL 2.25 2.27 2.39 Benzene 71-43-2 1.198 0 GM 2230 2240 2290

NL 1.99 2 2.04 Cyclohexanone 108-94-1 1.207 0 GM 915 915 915

NL 2.33 2.36 2.46 Trichloroethylene 79-01-6 1.395 0 GM 3100 3170 3230

NL 2 2 2.04 Cresol mixtures (Tricresol) 1319-77-3 1.414 0 GM 1200 1220 1250

NL 2.4 2.43 2.53 Tetrachloroethylene (perchloroethylene) 127-18-4 1.509 0 GM 3180 3180 3220

NL 2.25 2.26 2.28 Toluene 108-88-3 1.846 0 GM 2480 2490 2530

NL 2.33 2.34 2.37 Chlorobenzene 108-90-7 2.189 0 GM 3570 3580 3610

NL 2.48 2.48 2.49 Ethylbenzene 100-41-4 4.141 0 GM 8630 8700 8850

NL 2.34 2.34 2.36 Xylenes (total) 1330-20-7 4.766 0 GM 9480 9550 9680

NL 2.47 2.48 2.5 Dichlorobenzene, 1,2- (o-) 95-50-1 4.775 0 GM 9.66E+03 9.78E+03 9.88E+03

NL 2.52 2.58 2.75 Acetone 67-64-1 84.4 0 GM 7370 7660 7860

NL 2.58 2.67 2.83 Methylene chloride (dichloromethane) 75-09-2 1.027 2.30E-04 GM 7850 8050 8390

NL 2.14 2.15 2.18 Trichloroethane, 1,1,2- (vinyl trichloride) 79-00-5 1.169 7.48E-04 GM 2550 2590 2660

NL 7.09 7.15 7.57 Ethyl acetate 141-78-6 1.007 7.94E-02 GM 5.3E+10 5.3E+10 5.3E+10

a Based on median aquifer values.

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Table 3-9. Groundwater Pathway DAF Eff 50

Hydrolysis a Peak Child Adult
Solvent CAS No. Retardation a [yr -1 ] Liner DAF Eff 10 DAF Eff 10 DAF Eff 10 NL 32 32.1 33 Methanol 67-56-1 1 0 GM 1.91E+12 2.49E+12 2.96E+12
NL 38.9 39.4 40.2 Ethoxyethanol, 2- (ethylene glycol monoethyl ether) 110-80-5 1.001 0 GM 1.06E+12 1.23E+12 1.27E+12
NL 59.1 59.9 64.7 Methyl ethyl ketone 78-93-3 1.003 0 GM 1.94E+12 2.3E+12 2.52E+12
NL 69.8 71.7 78.4 Isobutyl alcohol 78-83-1 1.009 0 GM 1.31E+10 1.36E+10 1.4E+10
NL 2.06 2.08 2.18 Butanol, n- (n-butyl alcohol) 71-36-3 1.01 0 GM 1270 1280 1280
NL 56.8 57.8 61.3 Methyl isobutyl ketone 108-10-1 1.023 0 GM 9.36E+11 1.23E+12 1.38E+12
NL 47.5 48.3 49.7 Benzene 71-43-2 1.198 0 GM 1.94E+12 2.3E+12 2.48E+12
NL 33.6 33.9 34.5 Cyclohexanone 108-94-1 1.207 0 GM 4.21E+09 4.6E+09 4.67E+09
NL 52.5 53 55.2 Trichloroethylene 79-01-6 1.395 0 GM 7.97E+12 9.16E+12 1.04E+13
NL 34.4 34.5 34.7 Cresol mixtures (Tricresol) 1319-77-3 1.414 0 GM 8.99E+11 1.03E+12 1.19E+12
NL 52.7 53.6 55.4 Tetrachloroethylene (perchloroethylene) 127-18-4 1.509 0 GM 1.05E+13 1.12E+13 1.23E+13
NL 47.9 48.3 49.7 Toluene 108-88-3 1.846 0 GM 2.19E+13 2.48E+13 2.68E+13
NL 43.2 43.4 44.3 Chlorobenzene 108-90-7 2.189 0 GM 1.31E+14 1.34E+14 1.42E+14
NL 71.5 72 72.7 Ethylbenzene 100-41-4 4.141 0 GM 1.66E+15 1.82E+15 2.19E+15
NL 65.5 65.7 66.4 Xylenes (total) 1330-20-7 4.766 0 GM 5.84E+15 7.04E+15 7.1E+15
NL 68 68.1 68.8 Dichlorobenzene, 1,2- (o-) 95-50-1 4.775 0 GM 7.40E+15 9.17E+15 1.06E+16
NL 51.3 52.2 56.2 Acetone 67-64-1 84.4 0 GM 9.21E+12 1.24E+13 1.31E+13
NL 62.5 63.5 67.5 Methylene chloride (dichloromethane) 75-09-2 1.027 2.30E-04 GM 3.33E+16 5.85E+16 8.07E+16
NL 50.9 51.1 52.4 Trichloroethane, 1,1,2- (vinyl trichloride) 79-00-5 1.169 7.48E-04 GM 1E+30 1E+30 1E+30
NL 31700 32300 34200 Ethyl acetate 141- 78-6 1.007 7.94E-02 GM 1E+30 1E+30 1E+30

a Base on median aquifer values

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3.3.1.2 Key Assumptions This section presents key assumptions a ssociated with the groundwater modeling approach. More comprehensive documentati on of the EPACMTP model and associated assumptions are available in the EPACMTP Technical Background Document (U.S. EPA, 2003c).

 The model assumes that the vertical migrati on is 1-D and that transverse dispersion is negligible in the unsaturated zone.

 The model assumes linear and equilibrium so rption and homogeneous aquifer conditions.

 The model assumes that receptors use the uppermos t aquifer, rather than a deeper aquifer, as a source of drinking water. Uppermost aquifers can be used as a drinking water source, depending on various char acteristics such as the permeability of local soils. This assumption could overestimate risks in cases in which the uppermost aquifer is not used.

 The model assumes that long-term average conditions are sufficient and that shorter frequency fluctuations (e.g., in rainfall/infi ltration) are insignif icant in estimating long-term risk.

 Biodegradation in groundwater was exclude d given its significant variability and uncertainty.

 Preferential flow in karst aquifers or in fr actures was not considered, although such conditions are known to exist over broad areas. Preferential flow can allow contamination to migrate faster and at a higher concentr ation than in a standard porous medium. However, the contamination typically does not spread over such a broad area. As a result, the modeling may under- or overestimate the concentrations in groundwater.

 The distribution of receptor well distances and angles from the source was based on an approach described in Section 3.3.1.1 and in the EPACMTP Parameters/Data Background Document (U.S. EPA, 2003a). It is assume d that the distribution provides a reasonable representation of nationally variable conditions.

3.3.2 Model Inputs Appendix F presents the input values and the distributions used in modeling the groundwater pathway using EPACMTP. Below is a description of some key EPACMTP inputs. One of the most important inputs for EPACMT P is receptor location, which for this risk analysis includes residential drinki ng water wells. As discussed in Section 3.3.1.1 , the well was placed at a downgradient distance of up to 1 mile, based on a nationwide distribution of the nearest downgradient residential wells from a su rvey of Subtitle D MSWLFs (U.S. EPA, 1993a). The leachate fluxes (g/m 2 /yr) estimated by the landfill source model were used as input to the groundwater fate and transport model to generate constituent concentrati ons at receptor wells and associated groundwater DAFs. All leachate fluxes from the source model were applied uniformly over the footprint of the landfill at the top of the unsaturated soil column. Section 3.3.1.1 provides a detailed discussion of how th e leachate fluxes were used in groundwater pathway simulations.

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EPACMTP requires information about soil and aquifer properties as model inputs. For soils, EPACMTP uses soil texture as a key to gene rate consistent hydrological properties for the unsaturated zone model. As shown in the Vadose section of Table F-1 in Appendix F , sitespecific soil texture (WMU variable ISTYPE 1) was used to dete rmine appropriately parameterized distribution functions for unsaturat ed zone model input parameters from which random values for each input were selected. These model inputs include saturated hydraulic conductivity (SATK), van Genuchten soil moistu re parameters (ALPHA and BETA), residual and saturated water contents (WCR and WCS, re spectively), percent organic matter (POM), and oil bulk density. Similarly, the hydrogeological setting assigne d to each landfill was used to select appropriate aquifer conditions from EPACMTP’s HGDB (see Table F-2 in Appendix F ). Given an aquifer code setting for a la ndfill (WMU variable IGWR), a co rrelated sample of key aquifer model input parameters (hydrau lic conductivity [XKX], hydraulic gradient [GRADNT], depth to the water table [DSOIL], and saturated thickn ess [ZB]) was selected from a population of samples taken from similar hydrogeo logical settings. Details of the da ta used to parameterize the unsaturated zone and the development and use of the HGDB are given in the EPACMTP Parameters/Data Background Document (U.S. EPA, 2003a). Recharge is water percolating through the soil to the aquifer outside the footprint of the WMU. Typically, EPACMTP selects a recharge ra te using a meteorological station assignment (based on the geographic location and topograp hy of a WMU setting) and by the WMU’s associated soil texture mentioned above. Usi ng the soil texture and station assignment, a recharge rate was selected from a database of HELP model–derived recharge rates for climate stations across the coun try and for various soil textures. Fu rther details about how these rates were determined and other options for determ ining recharge rates outside of the EPACMTP model can be found in the EPACMTP Parameters/Data Background Document (U.S. EPA, 2003a).

3.3.3 Model Outputs The output of EPACMTP is a prediction of th e contaminant concentration arriving at a downgradient groundwater receptor location, an d is a time-dependent concentration, corresponding to the finite source scenario. The model can calculat e both the peak concentration arriving at the well and maximum time-averaged concentrations. In this analysis, maximum time-averaged concentrations were calculated fo r all solvents based on the exposure duration for each receptor type. Because the subsurface migra tion of some highly sorbing solvents may be very slow, it may take a long time for the plum e to reach the receptor well, and the maximum exposure may not occur until a ve ry long time after the WMU ceases operations. This time delay may be on the order of thousands of years. In this analysis, a time period of 10,000 years following landfill closure was modeled. If the mode l predicts that the maximum exposure will not have occurred after 10,000 year s, the actual receptor concentr ation at 10,000 years was used in the risk calculations.

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### 3.4 Shower Modeling
This section describes the methodology and the models that were used to predict the concentration of solvents in shower and bathr oom air, to which a receptor would be exposed while showering. Section 3.4.1 provides an overview of the conceptual model, including the basic approach and assumptions. Section 3.4.2 discusses the input parame ters and values used in this risk analysis. Section 3.4.3 discusses the model outputs. Appendix H provides additional information about the shower model:

 Appendix H , Equations, presents the equations that were used to estimate shower air
exposures.
3.4.1 Conceptual Model When present, residential groundwater well s typically serve as a household’s primary water supply, which is used for showering, as well as other general purposes. Because the solvents considered in this anal ysis are volatile, the potential inhalation expos ure associated with constituent volatilization that occurs during showering is of particular concern for the groundwater pathway.

3.4.1.1 Overview The shower model calculates the average air co ncentration of a contaminant to which the receptor is exposed associated with showering, including both time spent in the shower and time spent in the bathroom immediately after a s hower. Exposure to solvents released during showering was evaluated for adult residents on ly. Young children were assumed primarily to take baths and thus not encounter significant ex posure to solvents released while showering. The shower methodology implemented in this anal ysis is consistent with those applied in several previous risk analyses , including the Dye and Pigment risk analysis (U.S. EPA, 2003b). Appendix H presents the algorithms that form the basis of the shower model. The exposure factors used in the shower model, most of which are from U.S. EPA Exposure Factors Handbook (U.S. EPA (1997e), are documented in Appendix I . The times spent showering, in the shower stall after showering, and in the bathroom were va ried; the rest were fixed. The shower model estimates the change in th e shower and bathroom air concentrations over time based on the mass of constituent lost by the water and the air exchange rate between the shower, the bathroom, and the re st of the house. The shower m odel used for this analysis is based McKone (1987) and Little (1992a). The di fferential equations were solved using finite difference numerical integration.

The shower model estimates the concentration of a constituent in shower and bathroom air that results from the mass transfer of the c onstituent from water to shower air. The following equation estimates the overall mass transfer coeffi cient from tap water to air from showering:

1 3/23/2 15.2

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

′ +×= HDD K

a w ol β (Equation 3-3)

where

K

ol

= overall mass transfer coefficient (cm/s) β

= proportionality constant (cm-s) -1/3

Do not cite or quote. 3-43Review Draft Section 3.0 Analysis

D w = diffusion coefficient in water (cm 2 /s) (contaminant specific, provided in Appendix B ) D a = diffusion coefficient in air (cm 2 /s) (contaminant specific, provided in Appendix B ) H’ = dimensionless Henry’s law constant (calculated using the equation in Appendix H ).

Equation 3-3 was presented in McKone ( 1987) with the proportionality constant, β

, noted as a dimensionless value. In a correspondence to the editor, Little (1992b) indicated that β is not dimensionless, but has units of (cm-s) -1/3 as noted above. Because McKone did not provide an estimate for β , a value was derived using data for benzene. The modeled results using the benzene-derived value, 216 (cm-s) -1/3 , compared favorably to experimental data for several organic compounds of varying volatility (Coburn, 1996).

The constituent emission rate was estimate d from the change in the shower water concentration as the water falls, which is calculat ed using the overall mass transfer coefficient as follows:
⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

−=
' H yC
V A K dt dC

s ol (Equation 3-4)

where

C

= liquid-phase (droplet) constituent concentration ( μ g/cm 3 or mg/L)
t = time (s) K

ol

= overall mass transfer coefficient (cm/s) A = total surface area for mass transfer (cm 2 ) V

= total volume of water within the shower compartment (cm 3 )
y s = gas-phase constituent concentration in the shower ( μ g/cm 3 or mg/L) H

́ ’

= dimensionless Henry’s law constant (calculated using the equation in Appendix H ).

Consequently, in addition to the overall mass tr ansfer coefficient, the emission rate of a contaminant within the shower is dependent on the surface-area-to-volume ratio of the shower water and the concentration driv ing force between the water and the shower air. Equation 3-4 above is equivalent to Equation 4 presented in Little (1992a). The shower emissions were modeled based on falling droplets as a means of estimating the surface-area-to-volume ratio for mass transfer and the residence time of the water in the shower compartment. Equation 3-4 was integrated assuming the constituent concentration in the gas phase, y s , is constant over the timeframe of the dropl et fall. The time required for a droplet to fall equals the distance traveled (nozzle height) divided by the water drop let velocity. The ratio of the surface-area-to -droplet volume ( A/V ) equals 6/ d p (assuming a spherical shape where d is droplet diameter). Given the assumption that the drops fall at terminal velocity, the surface-areato-volume ratio and the residence time were based solely on droplet size. A droplet diameter of approximately 1 mm (0.098 cm) was selected. The term inal velocity for the selected droplet size was approximately 400 cm/s. The fraction of cons tituent emitted from a water droplet at any given time was calculated by integrating Equation 3-4 and rearranging as follows: 

## ( ) 

# ( N

sat
in out

sem ef C

C f

− −−= −

= 11 1

, 

# ) (Equation 3-5)

where f

em,s = fraction of constituent emitted from the shower droplet (unitless)

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C

out = droplet constituent concentration at shower floor/drain (mg/L) C

in = droplet constituent concentration entering the shower (mg/L) f

sat

= fraction of gas-phase saturation (un itless; calculated using the equation in Appendix H ) N

= dimensionless overall mass transfer coe fficient (calculated using the equation in Appendix H ).

Equation 3-5 is equivalent to Equation 5 in Little (1992a) for liquid-phase concentration change, except that Little used the tota l surface area for mass transfer (lumped with K ol ) and the shower flow rate to calculate an ove rall mass transfer coefficient, where N = ( K

ol ×A)/Q L ) and Q L is the volumetric flow rate of the water. Howe ver, the surface area of the droplet was a difficult value to obtain, so the equa tion for N was rearranged as N = ( K

ol )( A/V )(

DropRT ), where the drop residence time (DropRT) represents the time it takes for a dr oplet to fall. This eliminated the need for total surface area. The drop residence time was calculated using the height and the velocity of the droplet ( h/v t ), and the area-to-volume ratio (A/V) was set to 6/ d p (as described above, see also Appendix H ). After calculating the fraction of constituent emitted from the shower water, the model converts the fraction to a mass emitted for each time step. The mass was calculated by multiplying the total mass entering the shower during a given time step by the fraction emitted. The total mass entering the shower was determ ined by multiplying the concentration in the shower water by the flow rate of the shower head and the length of the time step. A time step of 0.2 minutes was used in the model. The gas-phase constituent concentration in the shower was calculated for each time step for the duration of the shower. The air exchange rate between the shower and the bathroom was included in the es timation of the gas-phase concentration of the solvents in the shower, as follows: 

# ( ) 

# ( ) 

# [ ]

y y E Q y y ts

V s, t ts s, t sb s, t b, t

s + = + −×− ×

× 1000 (Equation 3-6)

where

y s, t+ts = gas-phase constituent concentration in the shower at the end of time step (mg/L)
y s, t = gas-phase constituent concentration in th e shower at the beginning of time step (mg/L) Q

sb = volumetric gas exchange rate betw een shower and bathroom (L/min)
y b, t = gas-phase constituent concentration in the bathroom at the beginning of time step (mg/L)
ts = time step (min) = 0.2 E

= mass of constituent emitted from shower during time step (mg) V

s = volume of shower stall (m 3 ) 1000 = conversion factor (L/m 3 ).

The shower model also estimates bathroom exposure. The gas-phase constituent concentration in the bathroom was estimated as follows for each time step of the exposure duration: 

# ( ) ( ) 

# ( ) 

# [ ]

y y Qy y Qy y t

V b t ts b t sb s t ts b t bh b t h t

b , , , , ,,

+ + = + × − −× − ×

× 1000 s

(Equation 3-7)

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where

y b, t+ts = gas-phase constituent concentration in the bathroom at end of time step (mg/L)
y b, t = gas-phase constituent concentration in the bathroom at beginning of time step (mg/L) Q bh = volumetric gas exchange rate between bathroom and house (L/min) Q

sb = volumetric gas exchange rate be tween shower and bathroom (L/min)
y s, t+ts = gas-phase constituent concentration in the shower at end of time step (mg/L)

y h, t = gas-phase constituent concen tration in the house at beginning of time step (mg/L)
ts = time step (min) = 0.2 min V

b = volume of bathroom (m 3 ) 1000 = conversion factor (L/m 3 ).

The gas-phase constituent con centration in the house was assumed to be negligible in comparison with the gas-phase constituent concen tration in the bathroom. Therefore, the gasphase concentration of the constituent in the house ( y h, t ) was set to zero. The average air concentrations in the shower and bathroom were calculated by averaging the concentrations from each time step over the duration of the shower and bathroom use. From the average shower concentration and the aver age bathroom concentration, a time-weighted average concentration for an entire day was cal culated. This time-weighted concentration was used to estimate chronic health impacts due to inhalation expos ures from showering.

3.4.1.2 Key Assumptions The key assumptions, uncertainties, and limita tions associated with the shower model that are not addressed elsewhere in this document are as follows:

 Adult residents are assumed to take one shower per day.

 The shower model assumes the specified fr equency and duration of exposure (showers 350 days/year lasting 1 to 60 minutes each).

 The shower model assumes the mathematical formulation of McKone (1987) and Little (1992a).

3.4.2 Model Inputs The shower model requires inputs on exposure parameters specific to shower exposure (e.g., time spent showering, time spent in the bathr oom after showering), shower properties (e.g., shower and bathroom volume, nozzle height, fl ow rate), droplet properties (diameter and velocity), and air exch ange rates (e.g., between shower a nd bathroom). The exposure factors were represented with distributions, based on data in the Exposure Factors Handbook (U.S. EPA 1997e). For shower properties, we used fixed midrange values reporte d in the literature. Although fixing shower parameters does not capture variability in these i nputs when the model is run, the results compare favorably to experiment al data for numerous organic compounds of varying volatility (Coburn, 1996). The droplet properties (diameter and velocity) were also held constant, with their values based on a correlation to existing data. The volumetric exchange rates were derived from McKone (1987). The values us ed (300 L/min for the exchange rate between the bathroom and house, and 100 L/min for the exch ange rate between the shower and bathroom) fall within the range of values reported in a fi ve-study summary (Little, 1992a), which showed ranges of 35 to 460 L/min for the exchange between the shower and bathroom and 38 to 480 L/min for the exchange between the ba throom and the rest of the house.

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3.4.3 Model Outputs The outputs from the model are time-weighted average concentrations for an entire day, which were used to estimate chronic health im pacts due to inhalation ex posures from showering. As discussed above, the time-weighted average c oncentration for an entire day was calculated from the average shower concentration and the average bathroom concen tration. These values were calculated by averaging the concentrations from each time step over the duration of the shower and bathroom use.


### 3.5 Exposure Modeling
This section describes the methodology and th e model that was used to predict human exposure to chemical constituents after their release from a landfill into ambient air and groundwater. Section 3.5.1 provides an overview of the conceptual model, including the basic approach and assumptions. Section 3.5.2 discusses the input parameters and values used in this risk analysis. Section 3.5.3 discusses the model outputs. Appendices H and I , listed below, provide additional information about the exposure model and inputs:

 Appendix H , Equations, presents the equations that were used to model human exposure.

 Appendix I , Human Exposure Factors, presents the exposure factors used as input to the model.

3.5.1 Conceptual Model Human exposure modeling was conducted to estimate the magnitude, frequency, and duration of an individual’s exposure to solven ts from contaminated wi pes and laundry sludges in landfills. The term “exposure” refers to the conditi on that occurs when a contaminant comes into contact with the outer boundary of the body (see EPA guidelines for exposure assessment, 57 FR 22888-22893). An individual’s exposure to a contaminant completes an exposure pathway (i.e., the course a constituent takes from the landf ill to an individual) . Once the body has been exposed, the constituent can cross th e outer boundary and enter the body.

3.5.1.1 Overview

Exposure Pathways Human receptors may come into contact with solvents released from a landfill through a variety of pathways. The three exposure pathways included in the analysis were identified based on the solubility and volatility of the solvents being considered:

 Inhalation of Ambient Air. Both vapors and particles can be inhaled in ambient air by a receptor, but given the physical characteristics of the solvents modeled, only vapors were considered in this analysis.

 Inhalation of Indoor Air During Showering. Groundwater can be contaminated from solvents leaching from the landfill into the vadose zone and migrating into the groundwater. Groundwater from an offsite well wa s assumed to be used in the residence for showering by the adult residential recepto r. Constituents can volatilize from shower water, thus resulting in inhalation exposures.

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 Ingestion of Drinking Water. In addition to inhalation of indoor air released during showering, ingestion of contaminated dr inking water from groundwater sources was considered. Groundwater from an offsite well was assumed to be used as a source of drinking water for residents.

 Dermal Exposure During Showering/Bathing. Dermal exposure while showering or bathing in contaminated groundwater was also considered. Groundwater from an offsite well was assumed to be used in the reside nce for showering (by the adult residential receptor) or bathing (by the ch ild residential receptor).

Exposure via groundwater pathways (i.e., inge stion of drinking water, inhalation during showering, and dermal exposure dur ing showering or bathing) was considered separately from exposure to ambient air in this analysis because the exposure time scales differ and because the groundwater plume occurs in a lim ited area relative to potential ambient air receptor locations. For instance, a significant fraction of the mass that will be released into the air from landfill operations will be released during the landfill’ s operating life (generally between 40 and 75 years); in contrast, significant constituent leaching from the bottom of a landfill and transport through the saturated zone to a receptor well is likely to occur on the order of hundreds to thousands of years. Furthermore, receptors fo r the ambient air pathway were located based on random selection of distance and direction, which may not necessarily coincide with the location of the groundwater plume or contaminated wells.

Receptors Adult and child residents were modeled as receptors in this analysis. Table 3-10 shows the specific exposure pathways th at were modeled for each type of receptor. Both adult and child residents were modeled for exposur e via inhalation of ambient ai r, ingestion of groundwater, and dermal exposure during showering (adults) or ba thing (children). Only adult residents were modeled for exposure to the groundwater pathway via the inhalation of vapors generated during showering with well water; children were assumed to take baths, not showers.

Table 3-10. Receptors by Exposure Pathway

Receptor Inhalation of Ambient Air Ingestion of Drinking Water Inhalation of Indoor Air (Shower) a Dermal Exposure to Groundwater (Shower/Bath)

Adult resident Υ Υ Υ Υ (shower)

Child resident Υ Υ Υ (bath)

a Showering is explicitly considered for adul ts and not for children; however, the results can be considered to bracket the risk for

this pathway. In terms of noncancer risk, the hazard quotient is calculated based on the air c oncentrations, which are the same regardless of age, and the time spent in the shower, which is based on data for all ages. For c ancer, adults are typically the most sensitive receptor bec ause of the longer exposure. Children are an important subpopulation to consid er in a risk analysis because they are likely to be more heavily exposed than adults; for example, compared to adults, children may drink more fluids per unit of body weight. This higher intake-rate-to-body -weight ratio can result in a higher ADD than adults’ experience. As chil dren mature, their physical characteristics and

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behavior patterns change. To capture these chan ges in the analysis, the life of a child was considered in stages represented by the followi ng cohorts: cohort 1 (ages 1–5), cohort 2 (ages 6– 11), cohort 3 (ages 12–19), and cohort 4 (ages 20 –70, adult). Associated with each cohort are distributions of values, called e xposure parameters that are require d to calculate an individual’s exposure. The exposure parameter distributio ns for each cohort reflect the physical characteristics and behavior patterns of the indi viduals in that age range . Distributions for each exposure factor appropriate for each cohort were derived; the distributions for cohort 4 (ages 20– 70) were used for the adult receptor. For all e xposure factors except th e child bathing duration, the distributions were based on data from the Exposure Factors Handbook (EFH, U.S. EPA, 1997c,d). The child bathing duration was based on data from the review draft of the ChildSpecific Exposure Factors Handbook (Child-Specific EFH, U.S. EPA, 2006d), because the data on this exposure factor from the EFH reflect s total bath/shower time, which would include activities such as drying, while the data from th e Child-Specific EFH on this factor reflects just time in the bathtub. The development of the child exposure parame ters consisted of two steps: (1) defining the start age of the child and (2) selecting the exposure duration of the child. To capture the children’s higher intake-rate-to- body-weight ratio, a start age of 1 was selected. The distribution of exposure duration for cohort 1 (ages 1–5) was us ed to define the child’s exposure duration for each of the 10,000 iterations in the probabilistic analysis.

Estimation of Dose The model estimates the dose to each receptor by combining exposure parameters with media concentrations. Estimates of exposure were based on the potential dose (e.g., the dose ingested) rather than the applied dose (e.g., the dose delivered to th e gastrointestinal tract) or the internal dose (e.g., the dose delivere d to the target organ). This is generally consistent with the exposure metric used in the toxicological benchmarks used for risk analysis (see Section 3.6 ). The approaches used to estimate groundw ater ingestion dose and dermal dose are provided below. Doses were not calculated fo r inhalation exposures, because the inhalation cancer risk equation (Equation 3-14 in Section 3.6 ) and the inhalation HQ equation (Equation 3-17 in Section 3.6 ) calculate directly fr om air concentration. Doses from groundwater ingestion were ca lculated by multiplying the contaminant concentration by the respective intake rate on a per-kilogram-of-body-weight basis. Doses were then summed for the period of time during which exposure occurred, resulting in an ADD received from ingestion exposure. The ADD was us ed to assess noncancer risk from ingestion of groundwater. For cancer effects, wh ere the biological response is described in terms of lifetime probabilities even though exposure may not occur ove r the entire lifetime, dos e is presented as an LADD. The LADD was used to assess cancer risk s from ingestion of groundwater. For the purposes of this analysis, the ADD of contaminan t from the consumption of drinking water was calculated as follows:

1000 × × ×

= BW FCRC ADD dw dw dw dw (Equation 3-8)

where

ADD dw = average daily dose (mg/kg-d) C dw = average concentration in drinking water (mg/L) CR dw = consumption rate of drinking water (mL/day)

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F

dw = fraction of drinking water that is contaminated (unitless) BW = body weight (kg) 1000 = conversion factor (mL/L).

The LADD used for assessing risks for carci nogenic effects from consumption of contaminated drinking water was calculated as follows:

365 × × ×

= AT EFED ADD LADD dw (Equation 3-9)

where

LADD = lifetime average daily dose (mg/kg-d) ADD dw = average daily dose (mg/kg-d) ED = exposure duration (yr) EF = exposure frequency (d/yr) AT = averaging time (yr) 365 = conversion factor (d/yr).

Doses from dermal contact with groundwater while showering or bathing were estimated and evaluated for both the child and adult resident. Dermal dose and risk estimates were developed based on guidance provided in Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment) Interim (RAGS Part E; U.S. EPA; 2004b). Cons istent with this guidance, the exposure scenario applied for the child was a bath ing event and for the ad ult, a showering event

. Appendix H presents the algorithms that form the basis of the dermal exposure and risk modeling. The exposure factors used in the dermal dose estimates are from RAGS Part E, the EFH (U.S. EPA, 1997e), and the Child-Specific EFH (U.S. EPA

, 2006c) and are documented in Appendix I . The chemical-specific parameters used in the calculations are presented in Appendix B

. For the purposes of this analysis, the DAD of contaminant from dermal exposure during showering or bathing was calcula ted for carcinogens as follows:

ATBW SAEFEDEVDA DAD event

× ××××

= (Equation 3-10)

where

DAD = dermal absorbed dose (mg/kg-d) DA event = absorbed dose per event (mg/cm 2 -event) EV = event frequency (events/day) ED = exposure duration (years) EF = exposure frequency (days/year) SA = skin surface area available for contact (cm 2 ) BW = body weight (kg) AT = averaging time (years). Body surface area and body weight are strongl y correlated (0.986; U.S. EPA, 1997e), and the EFH discusses that the use of the ratio of body surface area to body weight (SA/BW) can be more appropriate than treating these factors as independent variab les in conducting human exposure assessments. Therefore, we used data on SA/BW in Equation 3-10 (replacing SA in the numerator with SA/BW and elimin ating BW in the denominator).

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For noncarcinogens, it was assumed that the exposure duration, e xposure frequency, and averaging time terms would cancel, resulti ng in the following simplified equation for noncarcinogens: 16

BW SAEVDA DAD event × ×

= (Equation 3-11)

3.5.1.2 Key Assumptions Exposure modeling relies heavily on default assumptions concerning population activity patterns, mobility, dietary habits, body weights, and other factors. This section presents key assumptions associated with the applied modeling approach.

 Solvent exposure through the inhalation of ambient air was assessed separately from exposures to solvents in groundwater (via ingestion of drinking water, inhalation of volatile solvents duri ng showering, or dermal exposure to solvents during showering or bathing) due to assumed temporal and spatial differences in the exposures.

 Receptors were assumed to both live and work at the receptor locati on identified for them during site characterization. In some cases, this assumption may over-estimate exposures, such as when an indi vidual lives at the identified location within the study area but commutes to work, school or daycare out side of the study area. Alternately, this assumption might under-estimate some exposures, such as when an individual lives in a less-contaminated part of the study area, but commutes to a more contaminated part of the study area.

 For children, a start age of 1 year was selected.

 Showering was considered for adults and not for children; however, the results can be considered to bracket the exposure for this pathway. For noncancer effects, the HQ was calculated based on the air concentrations, whic h are the same regardless of age, and the time spent in the shower, which is based on da ta for all ages. For can cer effects, adults are typically the most sensitive recep tor because of the longer exposure.

 For most of the human exposure factors, distri butions were fitted to the data summaries from the EPA’s EFH

. The approach most often taken wa s to fit these distributions to selected percentiles reported in the EFH ra ther than to the raw data. This approach implies that fitting to percen tiles will produce essentially th e same distribution as fitting to raw data.

 The chi-square goodness-of-fit test was used to select the most appropriate statistical distribution for most of the e xposure factors. However, all of the statistics that were used to assess goodness of fit assume a random sample, which may or may not be a valid assumption for data presented in the EFH, b ecause those data are based on surveys that, in many cases, did not involve purely random samples (e.g., they used clustering and stratification, primarily for economic reasons ). For a national Monte Carlo simulation, this is considered to be a relatively mi nor—albeit unquantified—sour ce of uncertainty.

16 As stated in RAGS Part E (U.S. EPA, 2004b), averagin g time for noncarcinogens is equal to exposure duration expressed in days (ED x 365 d/yr). Assuming an exposure frequency for noncarcinogens of 365 d/yr, the terms ED, EF, and AT cancel.

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 The modeling approach used to estimate dos e via shower inhalation and dermal contact did not account for solvent mass lost in one of these exposure routes when calculating the other. This approach will result in an overestimation of dose.

3.5.2 Model Inputs The inputs to the exposure model are human exposure factors and media concentrations. Media concentrations for ambient air, groundwater, and indoor ai r (i.e., shower and bathroom) are discussed in Sections 3.2
, 3.3 , and 3.4 , respectively. The exposure fact ors used as input to the model are discussed generally below and presented in Appendix I

. Table 3-11 lists the exposure factors used in this analysis, al ong with the data sources and whether the data were represented as a di stribution or a fixed va lue in the Monte Carlo analysis. The approach used in determining these distributions and fixed valu es is consistent with the methodology applied in several previous EPA risk analyses, including the Dye and Pigment Listing risk analysis (RTI, 2003), which was ex tensively reviewed by independent experts and EPA’s Science Advisory Board.

Table 3-11. Human Exposure Factor Input Parameters and Data Sources

Parameter Variable Type Data Source
Body weight (adult, child) Distribution U.S. EPA (1997c)
Ingestion rate: drinking water (adult, child) Distribution U.S. EPA (1997c)
Time spent in shower stall after shower ing (adult) Distribution U.S. EPA (1997c)
Time spent in bathroom immediately following shower (adult) Distribution U.S. EPA (1997c)

Shower contact time (adult) Distribution U.S. EPA (1997e)
Bathing contact time (child) Distribution U.S. EPA (2006c)
Ratio of surface area to body weight (child) Distribution U.S. EPA (1997c)
Exposure duration (adult, child) Distribution U.S. EPA (1997e)
Exposure frequency (adult, child) Fixed (constant) Assumed value
Fraction contaminated: drinking water Fixed (constant) Assumed value
Event frequency: showering and bathing (adult and child) Fixed (constant) U.S. EPA (1997e)
Human lifetime (used in carcinogenic risk calculation) Assumed value Fixed (constant)

 Body Weights. Distributions of body weight were developed for adult and child receptors based on EFH data.

 Drinking Water Ingestion Rates. Distributions of drinking water ingestion rates were developed for adult and child receptors based on EFH data.

 Showering/Bathing Parameters. The time spent in shower stall after showering, times spent in bathroom immediately following show er, and the shower contact time were used in the calculation of shower inhalation exposure and were based on EFH data. The shower contact time was also used to estim ate the DAD for the adult. The bathing contact time, from the Child-Specific EFH, was used to estimate the DAD for the child.

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 Ratio of Surface Area to Body Weight . The ratio of surface area to body weight was used in the calculation of the DAD for both the adult and ch ild receptors and was based on EFH data.

 Exposure Duration. Exposure duration refers to the amount of time that a receptor is exposed to a contaminant source. For this analysis, exposure duration was assumed to correspond with the receptor’s residence ti me in the same house. Exposure durations were determined using EFH data on residen tial occupancy (U.S. EPA, 1997e). Because the data used to develop parameter information for resident receptors were age specific, separate distributions were developed for adult and child residents. Exposure durations used in this analys is are provided in Appendix I . The exposure duration was used to determine the length of time over which a person was exposed. In addition, exposure duration for each realization was used to calculate a time-weighted average concentration in the contact medium (e.g., groundwater) to en sure that the length of exposure and the constituent concentration to which a person is exposed were consistent.

 Exposure Frequency. Exposure frequency is the frequency at which the receptor is exposed to the contaminated source duri ng the exposure duration. Unlike exposure duration, which accounts for the amount of time the receptor spends at the point of exposure, the exposure frequency simply indica tes the number of days that the receptor is assumed to be at that location (versus some ot her location, such as another city or state). Exposure frequency was not assumed to vary, so distributions were not developed for exposure frequency. All receptors were assumed to be exposed to the contaminant source 350 days a year. This value is based on an assumption that indivi duals are away from their homes (e.g., on vacation) approximately 2 weeks each year but are otherwise in the receptor area for 24 hours per day.

 Lifetime and Averaging Time. Averaging time is the period of time over which a receptor’s dose is averaged. To evaluate ca rcinogens, total dose was averaged over the lifetime of the individual, which was assumed to be 70 years, to calculate the LADD (see Equation 3-9). To evaluate noncarcinogens, the ADD was used, which requires neither exposure duration nor averag ing time (see Equation 3-8).

These exposure factors were used to calculate the dose (for ingestion and dermal pathways) or risk (for inhalation pathways) ba sed on water or air con centration. The primary data source of human exposure model inputs us ed in this analysis was the EFH. The EFH summarizes data on human behaviors and char acteristics related to human exposure from relevant key studies and provi de recommendations and associated confidence estimates on the values of exposure factors. These data were carefully reviewed and evaluated for quality before being included in the EFH. EPA’s evaluation cr iteria included peer review, reproducibility, pertinence to the United States, currency, adequacy of the data collection period, validity of the approach, representativeness of the population, charac terization of the variabi lity, lack of bias in study design, and measurement error (U.S. EPA, 1997c,d). For probabilistic risk analyses, PDFs are developed based on the values in the EFH. Appendix I presents the exposure factors used in this analysis. These fact ors are the same as those applied in the Dye and Pigment Li sting risk analysis (U.S. EPA, 2003b). Appendix I also describes the rationale and data used to select the parametric models (i.e., gamma, lognormal, and Weibull) for those exposure factors that were varied in the probabilistic analysis.

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3.5.3 Model Outputs The outputs from the exposure model are receptor-specific estimates of exposure based on the potential dose ingested or dermally absorbe d. These dose estimates were used as input to the human health effects model. The LADD was used to assess cancer risks from ingestion exposure routes. The ADD was used to assess n oncancer risk from ingestion exposures. The DAD was used to assess cancer risk and noncance r hazard from dermal exposures. Inhalation cancer and noncancer risks were evaluated usi ng ambient air concentrat ions or shower air concentrations.


### 3.6 Human Health Effects Modeling
This section presents the human health be nchmarks and approach used to estimate potential health risks in the modeling simulation. Section 3.6.1 provides an overview of the conceptual model, including the basic approach and assumptions. Section 3.6.2 discusses the input parameters and values us ed in this risk analysis. Section 3.6.3 discusses the model outputs. The risk equations used in the human h ealth effects modeling are presented in Appendix H

. 3.6.1 Conceptual Model Human health effects modeling was performed to estimate cancer and noncancer health impacts due to direct inhalation of ambient air, ingestion of groundwater contaminated by leachate, inhalation of shower vapors, and derm al exposure to contaminated groundwater during showering or bathing. A chemical constituent’s abili ty to cause an adverse health effect depends on the toxicity of the particular constituent, th e route of exposure to an individual (ingestion or inhalation), the duration and intensity of exposur e, and the resulting dose that an individual receives. In this analysis, estimates of toxic ity (the human health benchmarks) and exposure doses or exposure concentrations were comb ined to calculate individual excess lifetime carcinogenic risk estimates and noncancer HQs ( Section 3.6.1.1 ). The toxicity of a constituent was defined in terms of constituent-specific human health benchmarks for each route of exposure for cancer and noncancer endpoints. The health be nchmarks used in this analysis are point estimates of carcinogenicity and noncancer toxicity, respectively, th at were used to quantify the cancer risk and noncance r hazard for chronic exposures predicted during the Monte Carlo simulation. The benchmarks were derived fr om toxicity studies on cancer and noncancer endpoints in animals, as well as human epidemiological studies. Human health benchmarks for chronic expos ures were used in this analysis to characterize the potential cancer risks and noncancer hazards associated with two management scenarios (lined and unlined landfills). Oral cancer slope factors (CSFs) and inhalation unit risk factors (URFs) were used to produce risk estimat es for carcinogens. Reference doses (RfDs) and reference concentrations (RfCs) were used to estimate the noncancer hazards from oral and inhalation exposures, respectively. For dermal e xposures, in the absence of chemical-specific absorption data, we conservatively assumed 100 percent absorption per guidance in RAGS Part E (U.S. EPA, 2004b) and used the oral CSF and RfD to estimate the cancer risks and noncancer hazards, respectively, from dermal cont act with contaminated groundwater.

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Human health benchmarks serve as the risk me trics related to the risk criteria presented in Section 1.0 , and in essence, determine how risks are interpreted when potential health effects are assessed. As discussed in Section 5.0 , the solvent-specific mass loadings were defined as loading limits that result in cance r risks estimated to be in the rang e of 1 chance in 10,000 to 1 chance in 1,000,000 and noncancer HQs estimated to be 1. Specifi cally, the risk metrics for this analysis for individual exposure pathways were as follows:

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance in 1 million for adults and children due to the inhalation of ambient air (based on a URF)

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance in 1 million for adults due to the combined e xposures resulting from ingestion of drinking water, inhalation of indoor air during s howering, and dermal c ontact with groundwater during showering, for residents on groundwater wells. The cancer risk is based on the oral CSF for ingestion and dermal exposur es and on the URF for inhalation exposure.

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance in 1 million for children due to the combin ed exposures resulting from ingestion of drinking water and dermal contact with groundwater during bathing, for residents on groundwater wells. The cancer ri sk is based on the oral CSF for ingestion and dermal exposures and on the URF for inhalation exposure.

 A ratio of 1 of the ambient air concentrati on to the noncancer benchmark (RfC) for adults and children due to the inhalation of ambient air

 A ratio of 1 of the estimated daily dose to the noncancer benchmark (RfD) for adults due to the combined exposures resulting from ingestion of drinking water, inhalation of indoor air during showering, and dermal cont act with groundwater during showering, for residents on groundwater wells

 A ratio of 1 of the estimated daily dose to the noncancer benchmark (RfD) for children due to the combined exposures resulting from ingestion of drinking water and dermal contact with groundwater during bathi ng, for residents on groundwater wells

 For noncarcinogens with the same target organ and endpoint for both inhalation and ingestion exposure, an HQ equal to 1 for a dults and children on gr oundwater wells due to the ingestion of drinking water (adult a nd child), inhalation of indoor air during showering (adult only), and dermal contact dur ing showering (adult) or bathing (child). The HQ is based on the ratio of the predicted dose or air concentrati on to the appropriate health benchmark for noncancer endpoints, the RfD for ingestion and dermal exposure and the RfC for inhalation exposure.

3.6.1.1 Overview

As discussed in Section 3.5.1.1 , the timeframe for environmen tal releases of chemical constituents from landfills is very different for air and groundwater. Conse quently, potential risks from the inhalation of ambient air contaminated with solvents and the exposure to contaminated groundwater were not combined. For the groundwater pathway, the cancer risks were combined for drinking water ingestion, inhalation during shower ing, and dermal exposure during showering for adults, and for drinking water ingestion and dermal exposure during bathing for child ren, provided that no

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portal-of-entry effect was reported for the specific chemical solvent. For solvents with both an oral CSF and an inhalation URF considered in this analysis, no portal- of-entry effects were reported, and therefore, total can cer risks were calculated. Fo r noncarcinogens, the noncancer hazards were combined for the three pathways only for solvents with inhalation and ingestion health endpoints indicating that the target organ and effect was independent of the exposure route. This was the case for benzene, cres ol (mixture), and me thyl ethyl ketone.

Cancer Risk Cancer risk was characterized by estimating the lifetime excess cancer risk, representing the increased probability of developing cancer over a lifetime as a result of exposure to the solvents. For the drinking water ingestion pathway, the lifetime excess cancer risk estimates were based on the LADD (see Equati on 3-9) and the oral CSF:

o oral CSF LADD Risk × = (Equation 3-12)

where

Risk oral = lifetime incremental cancer risk fr om the oral exposure route (unitless) LADD = lifetime average daily dose (mg/kg-d)

CSF o = oral cancer sl ope factor (mg/kg-d) -1

. For the groundwater dermal contact pathwa y, the lifetime excess cancer risk estimates were based on the DAD for cancer endpoints (s ee Equation 3-10) and the oral CSF:

o dermal CSFDAD Risk × = (Equation 3-13)

where

Risk oral = lifetime incremental cancer risk fr om the oral exposure route (unitless) DAD = dermal absorbed dose (mg/kg-d) CSF o = oral cancer sl ope factor (mg/kg-d) -1

.
For inhalation of ambient air and inhalati on of indoor air duri ng showering, lifetime excess cancer risk estimates were calculated as follows:

AT ED URFC Risk air air × × ×

= 1000 (Equation 3-14)

where

Risk air

= lifetime incremental cancer risk from the air exposure route (unitless) C

air

= concentration in air (mg/m 3 ) URF = inhalation unit risk factor ([ μ g/m 3 ] -1 ) 1000 = conversion factor ( μ g/mg) ED = exposure duration (yr) AT = averaging time (yr).

Solvent-specific individual lifetime excess canc er risks were generated for each receptor for inhalation, ingestion, and dermal pathway expo sures, and as suggested by the risk metrics, the pathway-specific lifetime exce ss cancer risks for the groundwat er pathway were summed to generate a total lifetime excess can cer risk for adults. As noted a bove, risks due to inhalation of ambient air were not summed with the groundwater pathway risks because these exposures were not predicted to occur in the same timeframe.

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Noncancer Hazard Noncancer hazard from drinking water inge stion was characterized based on the ADD during the period of exposure (see Equation 3-8) and the noncancer benchmark for the ingestion route (RfD):

RfD ADD HQ dw oral = (Equation 3-15)

where HQ oral = oral hazard quotient (unitless) ADD dw = average daily dose from consump tion of drinking water (mg/kg-d) RfD = reference dose (mg/kg-d).

This ratio is referred to as an HQ, and as a risk metric, is used to establ ish a threshold of concern for a specific health effect. Unlik e cancer risk estimates, HQs are not probability statements; rather, the RfD represents a daily exposure that is likely to be without appreciable risk of deleterious noncancer eff ects during a lifetime. Noncancer hazard from dermal exposure via showering or bathing was characterized based on the DAD during the period of expos ure (see Equation 3-11) and the noncancer benchmark for the ingestion route (RfD):

RfD DAD HQ dermal = (Equation 3-16)

where HQ oral = oral hazard quotient (unitless) DAD = average daily dose from consump tion of drinking water (mg/kg-d)

RfD = reference dose (mg/kg-d).

Noncancer hazard from air inhalation was base d on the average air concentration during the period of exposure and the noncancer be nchmark for the inhalation route (RfC):

RfC C HQ air air = (Equation 3-17)

where HQ air = air hazard quotient (unitless) C

air

= air concentration (mg/m 3 ) RfC = reference concentration (mg/m 3 ).

As with noncancer effects for the ingestion pa thway, this ratio is al so an HQ and defines an estimate that is likely to be without adverse effects. As with the carcinogens, hazards due to inhalation of ambient air and inhalation of indoor air during showering were not summed because th ese exposures were not predicted to occur in the same timeframe. Noncancer hazard estimates from groundwater ingestion were not added to the noncancer hazard estimates from inhalation of indoor air during showering because the critical effect and target orga ns upon which the RfDs and RfCs were based were different for each solvent.

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3.6.1.2 Key Assumptions This section presents key assumptions a ssociated with the applied risk modeling approach.

 Risk estimates presented in this document address hypothetical chronic exposures for these receptors and were designed to provide a realistic range of pot ential scenarios. Not all potential scenarios were evaluated; for ex ample, infants (0- to 1-year-olds) were not evaluated.

 The hierarchy of sources that was assumed and implemented for this analysis (U.S. EPA, 2003f) uses the Integrated Risk Information System as a first-tier source, Provisional Peer-Reviewed Toxicity Values as a second-tie r source, and other sources as third-tier sources. Confidence in human health benchmar ks can be associated with the date the benchmarks were derived (or last reviewed). As additional toxicological data become available, the database becomes more comp lete. Methodologies evolve over time, with improvements in existing methods (e .g., EPA’s 1994 RfC methodology) and the development of new health benchmark pr actices (e.g., benchmark dose methodology).

 Constituent-specific health benchmarks were used for all solvents. However, the RfD for m- and o-cresol was used as a surrogate for the cresol mixture assessed in the analysis; this is not thought to have intr oduced any significant uncertainty.

 Although receptors are likely to be exposed to mixtures of solvents, noncancer hazard and cancer risk were assessed separately for each solvent. Ho wever, groundwater pathway exposures were added for each solvent for cancer endpoints and noncancer endpoints, respectively. For example, cancer risks across groundwater exposure pathways (i.e., ingestion of drinking wate r and inhalation of shower ai r) were combined for adults (children were not assumed to be exposed vi a the inhalation of show er air), irrespective of the type of cancer caused by each rout e of exposure, possibly introducing some uncertainty in the mass loading rates.

 The potential for adverse health effects due to exposure to multiple solvents during the same exposure period was not evaluated; theref ore, we did not consider the additivity, synergism, or antagonism of effects associated with potential, conc urrent exposures to more than one solvent at a time. Noncancer hazard and cancer risk due to exposure to solvent “mixtures” may, therefore, have b een under- or overestimated. However, for noncarcinogenic risks, risks for a specific solvent from ingestion of groundwater were added to the inhalation and dermal risks from showering/bathing for that solvent, if the data indicated that the target organ and effect are independe nt of the exposure route (as was the case for benzene, cresols, and methyl ethyl ketone).

 Estimating dermal noncancer hazards and cance r risks with the unadjusted RfD and oral CSF benchmarks will in most cases result in an overestimation of human health impacts from dermal contact during a bathing or showering event.

 We assumed that the shower or bath wate r contacted 100 percent of the body surface area in both the shower and bathing scenarios. Assuming 100 percent coverage will result in an overestimation of impacts.

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3.6.2 Model Inputs The inputs to the human health effects modeli ng include estimates of toxicity (the human health benchmarks) and exposure doses or e xposure concentrations. The exposure or media concentrations for ambient air and indoor air are discussed in Sections 3.2 and 3.4 , respectively. Exposure doses are discussed in Section 3.5 . The human health benchmarks used as input to the model are discussed below. Human health benchmarks for chronic expos ures were used in this analysis to characterize the potential cancer risks and noncancer hazards associated with two management scenarios (lined and unlined landfills). Oral CSFs and inhalation URFs were used to produce risk estimates for carcinogens. RfDs and RfCs were us ed to estimate the noncan cer hazards from oral and inhalation exposures, respectively. The CSF is an upper-bound estimate (approximating a 95 percent confidence limit) of the increased human cancer risk from a lifetime expos ure to an agent. This estimate is usually expressed in units of proportion (of a population) affected per milligram of agent per kilogram body weight per day (mg/kg-d). The URF is th e preferred cancer benchmark for inhalation exposures, where the air concentrat ions inhaled are used as the measure of dose. The unit risk is the upper-bound lifetime excess cancer risk estimate d to result from continuous exposure to an agent at a concentration of 1 μ g/m 3 in air. That is, if the unit risk = 1.5 × 10 -6 per μ g/m 3 , then 1.5 excess cancer cases would be expected to de velop per 1,000,000 people if exposed daily to a concentration of 1 μ g of the chemical agent in 1 m 3 of air for a lifetime. Unlike RfDs and RfCs, CSFs and URFs do not represent “safe” exposure leve ls; rather, they relate levels of exposure to a probability of developing cancer. The RfD and RfC are the primary benchmarks used to evaluate noncarcinogenic hazards posed by environmental exposures to chemical constituents. The RfD and RfC are estimates (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without appreciable risk of deleterious noncancer effects durin g a lifetime. However, an aver age lifetime exposure above the RfD (or RfC) does not imply that an adverse hea lth effect would necessarily occur (U.S. EPA, 2007).

Several sources of human health benchmarks are available. As per Office of Solid Waste and Emergency Response (OSWER) Directive 92 85.7-53, EPA’s Integrated Risk Information System (IRIS) is the preferred source of huma n health benchmarks (U.S. EPA, 2003f). After IRIS, human health benchmarks were obtained from sources in the following order of preference:

 Provisional Peer Reviewed T oxicity Values (PPRTVs)

 Other sources include the following in order of preference (priority was given to those sources that were most curre nt, those for which the basis was transparent and publicly available, and those that had been peer reviewed):

− Agency for Toxic Substances and Disease Registry (ATSDR)

− California Environmental Protection Agency (CalEPA)

− Health Effects Assessment Summary Tables (HEAST). Integrated Risk Inform ation System (IRIS). IRIS is EPA’s official electronic repository of chronic human health benchmarks a nd represents EPA-wide consensus on critical human health effects associated with exposure to chemical constituents released into the environment (U.S. EPA, 2007). Benchmarks in IR IS have been extensively reviewed, and each

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file contains descriptiv e and quantitative information on potentia l health effects associated with the benchmark and other studies evaluated dur ing its derivation. IRIS includes oral CSFs, inhalation URFs, oral RfDs, and inhalation RfCs.

Provisional Peer-Reviewed T oxicity Values (PPRTV). EPA’s National Center for Environmental Assessment (NCEA)/Superfund Hea lth Risk Technical Support Center derives provisional RfCs, RfDs, and CSFs for certain chemical cons tituents. These provisional values have been externally peer reviewed but have not undergone EPA’s formal review process for inclusion in IRIS. CalEPA Cancer Potency Factors and Reference Exposure Levels. CalEPA has developed cancer potency factors for chemical constituents regulated under California’s Air Toxics “Hot Spots” program (CalEPA, 2007). The cancer potency fact ors are analogous to EPA’s oral and inhalation CSFs. CalEPA has also developed chronic inhalation reference exposure levels (RELs), anal ogous to EPA’s RfCs (CalEPA, 2007). CalEPA used EPA’s inhalation dosimetry methodology (U.S. EPA, 1994a ) in the derivation of chronic inhalation RELs. The cancer potency factors and inhalation RELs have under gone internal peer review by various California agencies and be en the subject of public comment.

ATSDR Minimal Risk Levels. The ATSDR minimal risk levels (MRLs) are substancespecific health guidance levels for noncarcinoge nic endpoints (ATSDR, 2007). An MRL is an estimate of the daily human exposure to a hazardous substance that is likely to be without appreciable risk of adverse noncan cer health effects over a specifi ed duration of exposure. MRLs are based on noncancer health eff ects only and are not based on a consideration of cancer effects. MRLs are derived for acute, intermediate, and ch ronic exposure durations for oral and inhalation routes of exposure. Chronic inhalation and oral MRLs are similar to RfCs and RfDs, respectively (i.e., ATSDR uses the no observed adverse effect level [NOAEL]/uncertainty factor approach); however, MRLs are intended to serve as screen ing levels and are expos ure duration specific. ATSDR also uses EPA’s inhalation dosimetry methodology (U.S. EPA, 1 994a) in the derivation of inhalation MRLs. When based on the same cr itical study, the MRLs have similar toxicity endpoints but may apply different uncertainty fa ctors than EPA’s RfDs and RfCs. MRLs are available on ATSDR’s Web site and are presented in detail in individual ATSDR toxicological profiles. MRLs have undergone internal peer re view and been the subj ect of public comment. Health Effects Assessment Summary Tables (HEAST). HEAST is a listing of provisional noncarcinogenic and carcinogenic health benchmarks (i.e., RfDs, RfCs, CSFs, and URFs) derived by EPA (U.S. EPA, 1997f). HEAST benchmarks are gradually being replaced by PPRTVs or new IRIS benchmarks, and as such, HE AST has become a legacy database that is regarded as a third-tier source of toxicity values.

The chronic human health benchmarks used in this analysis are summarized in Table 3-12 . This table provides the constituent’s name, Chemical Abstract Service Registry Number (CASRN), RfD (in un its of mg/kg-d), RfC (mg/m 3 ), oral CSF (CSFo) [(mg/kg-d) -1 ], inhalation URF [( μ g/m 3 ) -1 ], and reference (source) for each benchmark. The benchmark definitions and source descrip tions are as described above. Note that for tetrachloroethylene, in addition to the CalEPA cancer values that were used in the analysis (CSFora l of 5.4E-1 per mg/kg-day, URF of 5.9E-6 per μ g/m 3 ), EPA’s Office of Pollution Prevention and Toxic Substances develope d an inhalation cancer value that is based on the same study as the CalEPA values (U.S . EPA 1998). EPA’s Office of Research and Development released an update fo r tetrachloroethylene IRIS valu es, in external review draft

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Do not cite or quote. 3-61 form, for public and peer review (U.S. EPA 2008) that is undergoing review by a committee of the National Academy of Sciences, Board on Environmental Studies and Toxicology.

3.6.3 Model Outputs The risk model generated a distribution of risk estimates for each scenario (solvent, liner type, contact medium, exposure route, receptor). Total risks from the groundwater pathway were calculated by summing the risks due to ingestion of drinking water, inhalation of shower emissions (for adults only), and dermal exposure during showering (adults) or bathing (children). Risks due to inhalation of ambient air were c onsidered separately because these exposures typically do not occur in the sa me timeframe. As discussed in Section 5.0 , the resulting risk estimates were used in determining the solven t-specific mass loading limits. These limits are defined as the mass loadings that result in can cer risks no greater than 1 in 100,000 (1E-5) and noncancer HQs no greater than 1 at the 90th pe rcentile of the distribution of risks.Review DraftSection 3.0 AnalysisDo not cite or quote.3-62Table 3-12. Chronic Health Benchmarks Used in the AnalysisConstituentCASRNRfD(mg/kg-d)RfDRefRfC(mg/m3)RfCRefCSFo(permg/kg-d)CSFoRefURF(perμg/m3)URFRefAcetone67-64-19.0E-01IRIS3.1E+01ATSDRBenzene71-43-24.0E-03IRIS3.0E-02IRIS5.5E-021.5E-02aIRIS7.8E-062.2E-06aIRISButanol, n- (n-butyl alcohol)71-36-31.0E-01IRISChlorobenzene108-90-72.0E-02IRIS5.0E-02PPRTVCresol mixtures1319-77-35.0E-02IRISb6.0E-01CalEPACyclohexanone108-94-15.0E+00IRISDichlorobenzene, 1,2- (o-)95-50-19.0E-02IRIS2.0E-01HEASTEthoxyethanol, 2- (ethylene glycolmonoethyl ether)110-80-54.0E-01HEAST2.0E-01IRISEthyl acetate141-78-69.0E-01IRISEthylbenzene100-41-41.0E-01IRIS1.0E+00IRISIsobutyl alcohol78-83-13.0E-01IRISMethanol67-56-15.0E-01IRIS4.0E+00CalEPAMethyl ethyl ketone78-93-36.0E-01IRIS5.0E+00IRISMethyl isobutyl ketone108-10-18.0E-02HEAST3.0E+00IRISMethylene chloride(dichloromethane)75-09-26.0E-02IRIS4.0E-01CalEPA7.5E-03IRIS4.7E-07IRISTetrachloroethylene(perchloroethylene)127-18-41.0E-02IRIS3.0E-01ATSDR5.4E-01CalEPA5.9E-067.1E-7cCalEPAToluene108-88-38.0E-02IRIS5.0E+00IRISTrichloroethane, 1,1,2- (vinyltrichloride)79-00-54.0E-03IRIS5.7E-02IRIS1.6E-05IRISTrichloroethylene79-01-66.0E-01CalEPA1.3E-02CalEPA2.0E-06CalEPAXylenes (total)1330-20-72.0E-01IRIS1.0E-01IRISaHigh and low end of the range developed by EPA.bIRIS RfD for m- and o-cresol used as surrogate for all isomers.cIn addition to the CalEPA values shown andused in this analysis, risks were alsocalculated using an inhalation unit risk estimate (URF of 7.1E-7 perμg/m3) published by EPA’sOffice of Pollution Prevention and Toxic Substances (OPPTS) that is based on the same study (by the National Toxicology Programin 1986) as the CalEPA inhalation value.Review Draft Section 4.0 Risk Characterization



## 4.0 Risk Characterization
The goal of this analysis was to derive solvent-specific mass lo ading rates, in kg solvent per year, that represent the maximu m amount of solvent that can be managed annually in municipal landfills and be protective of public health. Fo r the purpose of this analysis, EPA defines “protective” in terms of specific individual exces s cancer risk probability to be no more than 1 chance in 100,000, and for noncancer hazard, not exceedi ng a threshold dose or concentration. In the Guidance for Risk Characterization developed by the EPA Science Policy Council (U.S. EPA, 1995b), EPA defined the high end of the risk distri bution as being at or above the 90th percentile risk or hazard estimate generated during the Mo nte Carlo simulation. The high end of the risk distribution refers only to hypothetical individuals living near municipal landfills that may manage solvent-containing wipes and sludges. Thus, at a minimum, the mass loading rates were developed to ensure that solvents managed at 90 percent of the municipal landfills will not result in exposures for nearby residents above the risk criteria for cancer and noncancer endpoints. As discussed in Section 4.2 , several key assumptions and policy decisions bi as the risk analysis in the conservative direction, so that the mass load ing rates will tend to be more protective. Moreover, exposure pathways may not be complete at many landfill sites because not all residents at all sites will rely on groundwater; lacking receptor well s, the groundwater pathway risk at these sites will be zero. Given that the purpose of the risk analysis is to estimate allowable mass loading rates that are protective of public health, this sect ion first presents the results of the risk analysis. Specifically, we present the 50th and 90th percentile risk estimates based on a fixed input concentration using the methods and models described in Sections 2 and 3 for the Monte Carlo simulation. As explained in Section 2 , these results are not estimates of the actual risks to human health for solvent-containing wipes and sludges as they are currently managed. The 50th and 90th percentile risk estimates were selected from the modeled theoretical risk dist ributions. These estimates were compared to the risk/hazard criteria to de velop scaling factors used in the calc ulation of the risk-based mass loading rates (as described in Section 5 ). After presenting the risk results in Section 4.1 , we then discuss the strengths and limitations of th e risk analysis methodology in Section 4.2 , emphasizing the key uncertainties inherent in th e approach, models, and data used in this analysis.



### 4.1 Human Health Risk
This section presents the 50th and 90th percen tile cancer risk and noncan cer hazard estimates for the human exposure pathways evaluated in this analysis: (1) ambi ent air inhalation (2) groundwater-to-drinking-water i ngestion, (3) groundwater-to-show er inhalation, and (4) dermal exposure to groundwater during showering or bath ing. Because the risk results presented here represent the theoretical risk di stributions based on the unitized ma ss loading rate of 1 kg/year, not actual risk estimates, they should not be regarded as representative of the actual risks associated with wipes and sludge management. Further, it should be noted that the theoretical risk distributions are derived irrespective of the source of the solvent; the risk estimates are applicable to any combination of solvent mass contributed by either or both disposed wipes and laundry sludges. Section 4.1.1 presents the ambient air pathway results and Section 4.1.2 presents the groundwater pathway results.

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4.1.1 Ambient Air

Tables 4-1 and 4-2 present the 50th and 90th percentile ambient air inhalation risks for the adult and child receptors, respec tively. In each table, the results are presented by liner type (composite liner or no liner) and endpoint (cancer or noncancer). 17
The adult cancer risks are slightly higher than the child risks in all cases, primarily because the distribution of exposure duration for adults results in longer pred icted exposures for adults than for children. The child noncancer hazards (HQs) ar e slightly higher than the adult noncancer HQs because the noncancer risks are estimated by compar ing the ambient air concentrations with the solvent-specific RfC (see Section 3.6 ), and those air concentrations are averaged over the receptor’s period of exposure, centered on the peak (or maximum) am bient air concentration (see Section 3.2.3). In the case of noncancer end points, the shorter exposure dura tions for the child results in slightly higher average air concentrations and, consequently, higher risk estimates. Note that there is very little difference for the ambient air inhalati on risks between the noliner and composite-liner risk estimates. This is to be expected, because the dominant release mechanism for solvent vapors is advection, which is at a maximum just after the waste is applied and is influenced only to a limited extent by the cover/ liner type. The no-liner ri sks are slightly higher than the composite-liner risks probably because no-lin er landfills are covered with only a soil layer after closure (vs. geomembrane and soil for the lined landfills), and therefore, the diffusive losses are likely to be somewhat higher for the no-liner land fills (soil cover) than for the composite-lined landfills (geomembrane and soil cover). This diffe rence leads to greater diffusive losses to the atmosphere in the no-liner scenario after landfill closure.

4.1.2 Groundwater

Tables 4-3 and 4-4 present the 50th and 90th percentile risks, respectively, for groundwater exposures to adult receptors. Tables 4-5 and 4-6 present the 50th and 90th percentile risks, respectively, for the child receptor. In all four tables, results are grouped by liner type (composite liner or no liner) and endpoint (cancer or noncan cer). Tables 4-3 and 4-4 present adult receptor results individually for each expos ure route (i.e., drinking water inge stion, shower inhalation, dermal bathing/showering) and combined risks (i.e., ad ding the risks from the drinking water, shower inhalation and dermal pathways) for all carcinogen s and a subset of noncarcinogens (i.e., benzene, cresol, and methyl ethyl ketone) that have RfD and RfCs with matching endpoints and no portal of entry effects. Tables 4-5 and 4-6 present child r eceptor results individually for each exposure route (i.e., drinking water ingestion and dermal bathing) and combined risks. In the case of the combined risks, the driving pathways are identif ied in parentheses in the tables. When reviewing Tables 4-3 through 4-6, note that the individual pa thway results (i.e., drinking water ingestion, shower inhalation, and shower dermal for the adult and drinking water ingestion, shower inhalation, and bath dermal for the child) and combined results are each associated with a specific simulation run ID (see Appendix A for additional details). In a Monte Carlo simulation, different run IDs are associated with a different set of parameter values even though, at selected percentiles, these values are almost id entical. For cancer and fo r noncancer endpoints that are the same for different routes of exposure, the implication of this approach is that the combined risk estimate for an integrated simulation uses a common set of input values and, as a result, the total risk due to concurrent exposure pathways may be s lightly different than a risk estimate derived by adding the results for the individual pathways th at were modeled independently. As one might

17 Two separate estimates are presented for benzene cancer risk s to reflect both the high and low ends of the range for inhalation and oral exposures. .

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expect, the driving pathway identified in the integr ated simulation may or may not be the same as the driving pathway identified by comparing the drinking water ingestion result to the shower inhalation result. Thus, the driving pathway can “flip” de pending on the specific set of parameter values.
Table 4-1. Summary of 50th and 90th Percentile Solvent Wipes

Human Risk at the Unitized Mass Loading Rate (1 kg / year) Adult Ambient Air Inhalation Pathway

50th Percentile Risk 90th Percentile Risk

CAS Chemical NL Composite NL Composite Cancer

79-00-5 1,1,2-Trichloroethane 3.0E-11 2.7E-11 2.7E-10 2.4E-10
71-43-2 (H) Benzene(high)* 6.9E-11 7.3E-11 5.9E-10 6.1E-10
71-43-2 (L) Benzene(low)* 1.9E-11 2.1E-11 1.7E-10 1.7E-10

75-09-2 Methylene chloride 4.5E-12 4.6E-12 4.1E-11 4.1E-11
127-18-4(H) Tetrachloroethylene(high)* 5.4E-11 5.7E-11 4.6E-10 4.8E-10
127-18-4(L) Tetrachloroethylene(low)* 6.5E-12 6.9E-12 5.5E-11 5.7E-11

79-01-6 Trichloroethylene 1.9E-11 2.0E-11 1.6E-10 1.6E-10
Noncancer

110-80-5 2-Ethoxyethanol 1.6E-10 1.5E-10 1.4E-09 1.3E-09

67-64-1 Acetone 4.0E-11 3.0E-10 3.0E-10 2.2E-09

71-43-2 Benzene 1.7E-06 1.9E-06 1.2E-05 1.2E-05

108-90-7 Chlorobenzene 6.5E-07 6.5E-07 4.5E-06 4.4E-06

1319-77-3 Cresols (total) 2.0E-10 1.8E-10 1.6E-09 1.4E-09

95-50-1 Dichlorobenzene, 1,2 7.2E-08 6.7E-08 5.1E-07 4.8E-07

100-41-4 Ethyl benzene 2.5E-08 2.4E-08 1.8E-07 1.7E-07

67-56-1 Methanol 3.1E-10 2.7E-10 2.3E-09 2.0E-09

78-93-3 Methyl ethyl ketone 1.0E-08 2.2E-09 7.1E-08 1.6E-08

108-10-1 Methyl isobutyl ketone 2.4E-09 2.2E-09 1.8E-08 1.7E-08

75-09-2 Methylene chloride 1.5E-07 1.5E-07 9.9E-07 9.9E-07

127-18-4 Tetrachloroethylene 1.8E-07 1.9E-07 1.2E-06 1.3E-06

108-88-3 Toluene 6.7E-09 6.6E-09 4.6E-08 4.5E-08

79-01-6 Trichloroethylene 9.1E-08 9.8E-08 6.2E-07 6.5E-07

1330-20-7 Xylene (mixed isomers) 1.2E-07 1.1E-07 8.5E-07 7.9E-07

* high = high-end cancer benchmark range low = low-end cancer benchmark range

Do not cite or quote. 4-3Review Draft Section 4.0 Risk Characterization

Table 4-2. Summary of 50th and 90th Percentile Solvent Wipes

Human Risk at the Unitized Mass Loading Rate (1 kg / year) Child Ambient Air Inhalation Pathway

50th Percentile Risk 90th Percentile Risk

CAS Chemical NL Composite NL Composite Cancer

79-00-5 1,1,2-Trichloroethane 1.2E-11 1.1E-11 1.2E-10 1.1E-10
71-43-2 (H) Benzene(high)* 2.9E-11 3.1E-11 2.8E-10 2.8E-10
71-43-2 (L) Benzene(low)* 8.3E-12 8.9E-12 7.8E-11 8.0E-11

75-09-2 Methylene chloride 1.8E-12 1.9E-12 1.8E-11 1.8E-11
127-18-4(H) Tetrachloroethylene(high)* 2.3E-11 2.5E-11 2.2E-10 2.2E-10
127-18-4(L) Tetrachloroethylene(low)* 2.8E-12 3.0E-12 2.6E-11 2.7E-11

79-01-6 Trichloroethylene 7.9E-12 8.5E-12 7.4E-11 7.6E-11
Noncancer

110-80-5 2-Ethoxyethanol 1.6E-10 1.5E-10 1.4E-09 1.3E-09

67-64-1 Acetone 4.2E-11 3.1E-10 3.1E-10 2.3E-09

71-43-2 Benzene 1.9E-06 2.1E-06 1.3E-05 1.3E-05

108-90-7 Chlorobenzene 6.9E-07 7.0E-07 4.8E-06 4.8E-06

1319-77-3 Cresols (total) 2.1E-10 1.9E-10 1.6E-09 1.5E-09

95-50-1 Dichlorobenzene, 1,2 7.7E-08 7.2E-08 5.5E-07 5.1E-07

100-41-4 Ethyl benzene 2.7E-08 2.6E-08 1.9E-07 1.8E-07

67-56-1 Methanol 3.2E-10 2.9E-10 2.4E-09 2.1E-09

78-93-3 Methyl ethyl ketone 1.1E-08 2.3E-09 7.6E-08 1.7E-08

108-10-1 Methyl isobutyl ketone 2.5E-09 2.3E-09 1.9E-08 1.7E-08

75-09-2 Methylene chloride 1.5E-07 1.6E-07 1.0E-06 1.0E-06

127-18-4 Tetrachloroethylene 2.0E-07 2.1E-07 1.3E-06 1.4E-06

108-88-3 Toluene 7.1E-09 7.0E-09 4.9E-08 4.8E-08

79-01-6 Trichloroethylene 1.0E-07 1.1E-07 6.7E-07 7.0E-07

1330-20-7 Xylene (mixed isomers) 1.3E-07 1.2E-07 9.0E-07 8.3E-07

* high = high-end cancer benchmark range low = low-end cancer benchmark range

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Table 4-5. Summary of 50th Percentile Solvent Wipes Human Risk at the Unitized Mass Loading Rate (1 kg / year) Child Groundwater Exposures

Risk
Drinking Water Dermal Bathing Combined DW/DM

CAS Chemical NL Composite NL Composite NL Composite
Cancer

79-00-5 1,1,2-Trichloroethane 3.0E-09 PI 3.9E-10 PI 3.5E-09 (DW) PI

71-43-2 (H) Benzene(high)* 7.5E-09 3.9E-18 1.6E-09 7.2E-19 9.5E-09 (DW) 4.5E-18 (DW) 71-43-2 (L) Benzene(low)* 2.0E-09 1.1E-18 4.3E-10 2.0E-19 2.6E-09 (DW) 1.2E-18 (DW) 75-09-2 Methylene chloride 7.3E-10 2.4E-24 3.8E-11 1.6E-25 7.8E-10 (DW) 2.5E-24 (DW) 127-18-4(H) Tetrachloroethylene(high)* 5.8E-08 1.4E-17 1.6E-08 4.2E-18 7.9E-08 (DW) 1.9E-17 (DW) 127-18-4(L) Tetrachloroethylene(low)* 5.8E-08 1.4E-17 1.6E-08 4.2E-18 7.9E-08 (DW) 1.9E-17 (DW) 79-01-6 Trichloroethylene 1.5E-09 3.0E-19 5.6E-10 1.2E-19 2.2E-09 (DW) 4.3E-19 (DW) Noncancer

79-00-5 1,1,2-Trichloroethane 2.1E-04 PI 2.8E-05 PI NA NA

110-80-5 2-Ethoxyethanol 4.9E-07 1.2E-16 3.3E-09 7.2E-19 NA NA

67-64-1 Acetone 1.2E-06 9.2E-18 8.0E-09 5.8E-20 NA NA

71-43-2 Benzene 5.5E-04 2.9E-13 1.2E-04 6.8E-14 6.8E-04 (DW) 3.6E-13 (DW) 71-36-3 Butanol 1.7E-06 4.1E-13 5.4E-08 1.2E-14 NA NA

108-90-7 Chlorobenzene 7.2E-05 1.8E-15 3.9E-05 9.4E-16 NA NA

1319-77-3 Cresols (total) 8.6E-06 3.3E-15 1.2E-06 5.0E-16 1.0E-05 (DW) 3.8E-15 (DW) 108-94-1 Cyclohexanone 2.7E-07 1.8E-14 7.0E-09 4.2E-16 NA NA

95-50-1 Dichlorobenzene, 1,2 7.7E-06 9.6E-19 7.8E-06 1.0E-18 NA NA

141-78-6 Ethyl acetate 6.7E-10 PI 1.4E-11 PI NA NA

100-41-4 Ethyl benzene 8.7E-06 1.8E-17 7.6E-06 1.5E-17 NA NA

78-83-1 Isobutanol 4.2E-07 1.7E-14 1.1E-08 4.4E-16 NA NA

67-56-1 Methanol 3.7E-06 8.8E-17 1.4E-08 3.5E-19 NA NA

78-93-3 Methyl ethyl ketone 1.2E-06 1.5E-16 1.7E-08 1.8E-18 1.3E-06 (DW) 1.5E-16 (DW) 108-10-1 Methyl isobutyl ketone 3.3E-06 9.5E-16 1.5E-07 4.0E-17 NA NA

75-09-2 Methylene chloride 2.5E-05 7.2E-20 1.4E-06 4.2E-21 NA NA

127-18-4 Tetrachloroethylene 1.8E-04 3.7E-14 5.1E-05 1.1E-14 NA NA

108-88-3 Toluene 2.0E-05 1.4E-15 1.0E-05 6.9E-16 NA NA

1330-20-7 Xylene (mixed isomers) 2.8E-06 8.2E-19 2.5E-06 6.7E-19 NA NA

(DM) - Driving pathway is dermal bathing * high = high-end cancer benchmark range(DW) - Driving pathway is drinking water low = lowend cancer benchmark range NA - Not applicable NHB - No health benchmark PI - Pathway incomplete because solvent does not reach receptor well during simulation.

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(DM) - Driving pathway is dermal bathing * high = high-end cancer benchmark range(DW) - Driving pathway is drinking water low = low-end cancer benchmark range NA - Not applicable NHB - No health benchmark PI - Pathway incomplete because solvent does not reach receptor well during simulation.

Do not cite or quote. 4-8Review Dra ft Section 4.0 Risk Characterization

Do not cite or quote. 4-9 The groundwater pathways for both the adult an d child included drinking water ingestion and dermal exposure via bathing (i .e., showering for the adult and bath for the child). Comparing adult to child results for the dri nking waster ingestion pathway, we see that the noncancer risks are higher for the child receptor, but the cancer risks ar e typically higher for the adult receptor. The adult cancer risk estimates are slightly higher because of the difference in the adult distribution for exposure duration as compared to the child. The child noncancer risks are higher because the ratio of consumption rate to body weight tends to be hi gher for younger children than for adults (see Sections 3.5 and 3.6 for drinking water dose and hazard quotien t calculations). Comparing adult to child results for dermal exposure, the dermal noncancer risks are slight ly higher for the child receptor, however the cancer risks for both receptors are very similar. The drinking water risks are higher than the dermal risk for all receptor/che mical/ endpoint combinations except for the child, noncancer hazard for 1,2-dichlorobenzene. In this case, the drinking water and dermal noncancer hazards are essentially the same.

The shower inhalation results for adults are presented with drinking water ingestion and dermal results in Tables 4-3 and 4-4. As noted above, the adult cancer and noncancer hazards for drinking water ingestion are alwa ys higher than the dermal estimates. The noncancer results for drinking water ingestion for adults were higher th an for shower inhalation for all solvents except benzene, chlorobenzene, 1,2-dichlorobenzene, ethy l benzene, methylene chloride, and xylene. The relatively high Henry’s law constants for these so lvents indicate that they are among the most volatile constituents, and therefore, the vapor phase concentration is likely to be much higher during showering. There is one exception to this tr end: the predicted drinking water hazard for tetrachloroethylene is higher than for showering despite this solvent’s relatively high Henry’s law constant. This is likely because th e toxicity of tetrachloroethylene is relatively high for the ingestion pathway as compared to its toxicity for the inhalation pathway (see Section 3.6.2 ). Similarly, the shower inhalation cancer risks are higher than the drinking water ingestion risks for all chemicals except tetrachloroethylene. As expected, the predicted groundwater pathway results for the composite-lined landfills are lower than the results for unlined landfills. In Ta ble 4-3, note that the 50th percentile results for 1,1,2-tricholoethane and ethyl acetate for the composite liner scenario show th at these two solvents did not reach the receptor well dur ing the 10,000 year simulation run using EPACMTP. Thus, for the groundwater results, it is also important to consider the travel time, that is, the number of years that the model predicts it will take the solvent to r each the receptor well after it leaches out from the bottom of the landfill. Estimates of peak concentration arrival times were generated for each Monte Carlo simulation of groundwater pathway scenarios (e.g., so lvent, liner type), and can be examined as a cumulative distribution of observations. Table 4-7 provides information on the elapsed travel time (in years) associated with peak exposure concen tration at the receptor well. The elapsed time is from the onset of leaching into the subsurface up un til the time the peak concentration was predicted to arrive at the well, for all groundwater pathwa y Monte Carlo realizations by solvent and liner scenario. The meaning of any indivi dual value shown in the table, w ith units of years, corresponds to a percentile can be interpreted as the maximum number of years it takes X percent of the peak observed concentrations to arrive at the receptor well for a given solvent/liner type set of Monte Carlo modeling realizations. The table indicates that, according to the modeling assumptions, the presence of a composite liner can significantly increase the predicted time required for a given solvent to reach the receptor well. However, beca use composite liners have only been in widespread use for a few decades at most, these modeli ng results must be in terpreted cautiously.Review DraftSection 4.0 Risk CharacterizationTable 4-7. Travel Time Associated with the PeakExposure Concentration at the Receptor WellPercentiles of Time to the Arrival of the Peak Observed Concentration of ContaminantReaching the Receptor Well (years)aConstituent NameCAS No.10%20%30%40%50%60%70%Linerb80%90%NL153143546579103150491Acetone67641GM000013336182122678441NL314454657895126193628Benzene71432GM0000242524108727378985NL364858688197127194848Butanol, n- (n-butyl alcohol)71363GM0002486151189238256019771NL415875941211612283861209Chlorobenzene108907GM0000327852192851289748NL41577391113144199306814Cresol mixtures (Tricresol)1319773GM0000315649136033909384NL4054668097119160254908Cyclohexanone108941GM0002927101402280865439793NL57871221682333294777882429Dichlorobenzene, 1,2- (o-)95501GM00004121514379184219817NL334453637488114166469Ethoxyethanol, 2-(ethylene glycol monoethyl ether)110805GM000017440181821147716NL00334453627391130Ethyl acetate141786GM00000001306NL53791071431972784136912162Ethylbenzene100414GM00004341413348481519812NL132541536580106161732Isobutyl alcohol78831GM0001955111077227954479746NL3548607184102132195463Methanol67561GM000020339074918456813NL142942536579103152577Methyl ethyl ketone78933GM000015740991624598692(continued)Do not cite or quote.4-10aftSection 4.0 Risk CharacterizationDo not cite or quote.4-11Table 4-7. (continued)Percentiles of Time to the Arrival of the Peak Observed Concentration of ContaminantReaching the Receptor Well (years)aConstituent NameCAS No.Linerb10%20%30%40%50%60%70%80%90%NL153144556781106157583Methyl isobutyl ketone108101GM000017644398025818711NL12274152647799142329Methylene chloride (dichloromethane)75092GM00007827967317936745NL3447597389112153247810Tetrachloroethylene (perchloroethylene)127184GM0000273637140035979500NL395569871081401983301032Toluene108883GM0000331769173345219700NL3048597286104134192402Trichloroethane, 1,1,2-(vinyl trichloride)79005GM0000129656312063660NL3246566984105142224735Trichloroethylene79016GM0000250583127632499383NL59901271722393334807982458Xylenes (total)1330207GM00004331548382684819819aA value of “0” indicates that no mass reached the water table, and a value of “1” indicates that mass reached the water tablebut not the receptor well.bNL = No Liner, GM = Geomembrane Composite LinerReview DrReview Draft Section 4.0 Risk Characterization



### 4.2 Uncertainty Characterization
The risk estimates in this section provided a great deal of informa tion about the relative nature of exposures and potential risks. For example, the central tendency results (50th percentile) suggested that the groundwater e xposure pathways are the driving pathways (i.e., highest risk) for the no-liner scenario, whereas the ambient air pathway is the predominant driving pathway for the composite-liner scenario. This is consistent with our expectations because the composite-liner should prevent or limit leaching to groundwater, reducing the potential for exposure via this pathway. Interestingly, we see that this trend did not hold tr ue for the 90th percentile results. At the high end of the risk distribution, the groundwater risk estimates for the composite-liner scenario increased dramatically, and it was apparent that ambient ai r inhalation was not always the driving pathway. Again, this is consistent with our expectations in a Monte Ca rlo simulation: the groundwater pathway risk estimates at the 90th percentile re present lower probability combinations of landfill characteristics (e.g., liner leakage) and environmen tal conditions (e.g., high inf iltration rates) that favor leaching and groundwater transport. Important ly, the relative difference between the 50th and 90th percentile values for the groundwater pathways is greater than it is for the ambient air inhalation pathway, suggesting that the variability in the groundw ater pathway simulations is significantly higher than the vari ability associated with the ambient air pathway simulations. Although the risk estimates provide substantial insight into the potential fo r exposure and adverse health effects, there are a number of uncertainties inherent in predictive risk assessment modeling. As suggested in Section 2.2.2 , this uncertainty characterization will primarily address Type A uncertainty, the uncertainty associated with variability. Variability represents true heterogeneity in characteristics, such as the range of body weights across an adult population or the hydrogeologic properties char acteristic of different regions across the country. Although variability may be known with great certainty (e .g., the age distribution of a population may be known and represented by the mean age and its standard deviation), it cannot be elim inated and should be treated explicitly in the assessment. Uncertainty is a desc ription of the imperf ection in knowledge of the true value of a particular parameter or ability of a model to accurately predict the beha vior of a particular solvent. In contrast to variability, uncertainty can be reduced through additional information data gathering, data analysis, or model research (i .e., better data, better models). Variability arises from true heterogeneity in characteristics, such as body weight differences within a population or differences in contaminant levels in the environment.

Uncertainty represents a lack of knowledge about factors such as the actual rate of biodegradation in the environment, which may be reduced with additional research to improve data or models.

In this section, we discuss three major sources of uncertainty: (1) the uncertainty associated with the conceptual approach developed for the risk analysis (e.g., how does the structure of the assessment influence the risk estimat es?), (2) the uncertainty associat ed with the models used (e.g., how well does a model represent real ity?), and (3) the uncertainty asso ciated with the distributions and values assigned to input parame ters, particularly those paramete rs to which the model is most sensitive (e.g., what are the true values of each pa rameter?). We present qualitative descriptions of uncertainty, indicating whether or not a particul ar assumption or key da ta input tends to be conservative and roughly what level of bias is likely to be introduced (e.g., more than an order of magnitude). As appropriate, we describe the strengths and limitations of various modeling

Do not cite or quote. 4-12Review Draft Section 4.0 Risk Characterization
components, noting where it was necessary to simplif y a mathematical construct or use a fixed value for an input parameter. And las tly, we offer several “side-analys es” that quantitatively address potential uncertainties associated with the analysis; in general, these side-ana lyses were developed to address peer-review comments on a previous draft of the technical support document.

4.2.1 Conceptual Approach

As discussed in Section 2 , the objective of this analysis wa s to support a national analysis of solvent-containing wipes and laundry sludge that ma y be managed in landfills, and to establish allowable mass loading rates for each solvent that would be protective of public health. To achieve this objective, we developed a concep tual approach that frames the risk analysis in terms of (1) the scenarios that were included in the model simula tions (i.e., what we mode led) and (2) the Monte Carlo framework used to generate the distributions of risk estimates (i.e., how we modeled it). Below, we discuss these two aspects of the conceptual approach, poi nting out several key uncertainties/limitations.

4.2.1.1 Modeling Scenarios The modeling scenarios were constructed to re present the potential hi gh-end risks to human health that could be attributed to the disposal of solvent-c ontaining wipes and laundry sludge in municipal landfills. The modeling scenarios describe th e chemical constituents of interest (specific solvents), the source of contaminant release (munici pal landfills with no liner and with a composite liner), the exposure pathways (e.g., tr ansport in air from the source to potential inhalation receptors), and the receptors (child and adult receptors associated with particul ar exposure pathways). However, this is not an exhaustive list of modeling scenarios, and using risk estimates from this subset raises several questions regarding the representativeness of the scenarios with respect to potential risks. That is, are these scenarios likely to capture the high-end risks to human h ealth associated with landfill management of solvent-containing wipes and laundry sludge? We identified the following issues as uncertainties/limitations inherent in the modeling scenarios chosen for this analysis.

 The analysis did not consider lateral migrati on of landfill gas from la ndfills in the subsurface and potential collection and exposure in build ings. Although this transport mechanism has been documented in particular cases, it is not be lieved to be a dominant transport pathway for most landfills. If lateral migration of landfill ga s results in vapor intrusion at concentrations above the levels predic ted for the shower scenario, risk estimates for the current scenarios may not be protective for this pathway.

 Potential mass release from landfills due to episod ic precipitation events was not modeled; it was implicitly assumed that existing landfill c ontrols (e.g., berms) would prevent any rain from “washing through” the landfill and possi bly delivering solvents to nearby surface waters. This is an issue of temporal scale, because short-term, acute exposures were not evaluated either for human health or the envi ronment. Mass loading rates developed for an episodic event scenario could be significantly different than the mass loading rates based on chronic risk; the relative values would depend on the e ndpoint considered (e.g., acute toxicity), the location of th e landfill (e.g., proximity to re sidents), and other scenario characteristics. Thus, the mass loading rates for long-term, low-level solvent releases could be over- or underprotective relative to catastrophic re lease scenarios.

Do not cite or quote. 4-13Review Draft Section 4.0 Risk Characterization

 Potential risks to aquatic life associated with surface water contamination due to groundwater interactions were not evaluated. Given the chemical and physical properties of the constituents, we assumed that natural dilution and attenuation mechanisms would result in negligible risks to aquatic bi ota. National Recommended Wate r Quality Criteria were not available for any of the solvent constituents; however, a quick comparison between Tier II water quality criteria (Suter and Tsao, 1996) and the Maximum Contaminant Levels (U.S. EPA, 2006d) for the solvents evaluated suggests th at risks to aquatic lif e will be below levels of concern if human health is adequately pr otected. Lack of a quant itative ecological risk screening is a limitation of our conceptual approach.

4.2.1.2 Monte Carlo Approach To address the uncertainty associated with va riability, we varied parameter values from realization to realization by randomly samp ling from parameter-specific distributions. 18 The sampling process accounted for correlated data (e.g., landfill characteristics), and importantly, maintained the spatial relationships among th e landfill location, climate region, and groundwater region. We developed information on the form and char acteristics of the data distributions for input parameters required by the various models. Howe ver, implementing the Monte Carlo framework also required us to determine how best to use av ailable information as well as how many realizations were sufficient. The implications of seve ral key decisions are discussed below.

 We selected the locations for MSWLFs base d on a distribution of 66 offsite Industrial D landfill locations, under the assumption that a na tional distribution of municipal landfills can be reasonably represented by a distribution of offsite industrial landf ills. Municipal landfill locations are likely correlated with population and industrial centers in a pattern similar to that of industrial landfills (e.g., a higher densit y in areas with greater population density). The lack of an alternative data source to repres ent the national distribut ion of MSWLF locations is a potentially significant source of uncerta inty; the location determines a number of environmental setting properties (e.g., depth to wa ter table), which in turn affect the risk estimation. This unquantified sour ce of uncertainty could re sult in either an over- or underprediction of risk, depending on the repres entativeness of the Industrial D data set.

 Several critical landfill characteristics (a rea, volume, operating life) were based on parametric statistics from the EPA-WBJ database 19 and not determined through sampling of actual landfills in the database . Given the proprietary nature of the EPA-WBJ database, we could not sample landfills directly. This w ould have been the preferred method, because sampling the EPA-WBJ landfill database would have created a data set of actual landfills. After several failed attempts to produce a correlated data set of landfill characteristics from the parametric statistics, we created a representa tive data set that adhe red to a set of basic engineering principles and r easonable minima and maxima fo r each landfill characteristic (e.g., landfill depth could not be 1 cm; active life could not be 2,000 years). A comparison of the resulting data set to available information on municipal landfills suggests that the data set is, in fact, representa tive (see Table 3-2).

18 For some parameters such as those that describe the hydroge ological setting, the distributions were allowed to co-vary as determined in readily available studies.
19 As discussed in Section 3.1 , the database obtained commercially by EPA from the Waste Business Journal (WBJ) and was analyzed and considered a representative data s ource for municipal landfill geometry and operating life characteristics.

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 The choice of 10,000 iterations in this analysis was based on our hist orical knowledge of conducting Monte Carlo simulations using these models for EPA risk analyses. Specifically, based on more than a decade of modeling expe rience, we concluded that 10,000 iterations are adequate to ensure stability of the results at the 90th percentile a nd other percentiles of interest. In response to peer-review comments , we evaluated this assumption by performing a stability assessment for two chemicals with ve ry different chemical- physical properties, 2- ethoxyethanol (least volati le) and tetrachloroethyle ne (most volatile). In essence, we sampled the results from 10,000 Monte Carlo simulations for several modeling sc enarios (e.g., unlined landfill, adult shower inhalation) to create simulations of 4,000; 6,000; and 8,000 realizations. The results shown in Figures 4-1 and 4-2 demonstrate that using10,000 iterations adequately ensures the stability of the results at the percentiles of interest (50th and 90th). At the 99th percentile, the results begin to deviate, indica ting that more realizations are required to ensure stability at th e extreme tail of the distribution. Figure 4-1. Comparison of different numbers of iterations for 2-ethoxyethanol (least volatile).

2-Ethoxyethanol Ambient Air/Adult

0.00E+00 1.00E-09 2.00E-09 3.00E-09 4.00E-09 5.00E-09 6.00E-09 7.00E-09 8.00E-09

50 60 70 80 90 100

PercentileNon-cancer risk 4000
6000
8000
10000 2-Ethoxyethanol Groundwater/Adult/Shower inhalation

0.00E+00 5.00E-07 1.00E-06 1.50E-06 2.00E-06

50 60 70 80 90 100

PercentileNon-cancer risk 4000
6000
8000
10000

(a) (b)

2-Ethoxyethanol Groundwater/Adult/Drinking water

0.00E+00 5.00E-05 1.00E-04 1.50E-04 2.00E-04 2.50E-04

50 60 70 80 90 100

PercentileNon-cancer risk 4000
6000
8000
10000 [combined not calculated for noncarcinogen ]

(c)

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Tetrachloroethylene Ambient Air/Adult

0.00E+00 5.00E-10 1.00E-09 1.50E-09 2.00E-09 2.50E-09

50 60 70 80 90 100

PercentileCancer risk 4000
6000
8000
10000 Tetrachloroethylene Groundwater/Adult/Shower inhalation

0.00E+00 2.00E-07 4.00E-07 6.00E-07 8.00E-07 1.00E-06 1.20E-06

50 60 70 80 90 100

PercentileCancer risk 4000
6000
8000
10000

(a) (b)

Tetrachloroethylene Groundwater/Adult/Combined

0.00E+00 1.00E-06 2.00E-06 3.00E-06 4.00E-06 5.00E-06 6.00E-06

50 60 70 80 90 100

PercentileCancer risk 4000
6000
8000
10000 Tetrachloroethylene Groundwater/Adult/Drinking water

0.00E+00 5.00E-07 1.00E-06 1.50E-06 2.00E-06 2.50E-06 3.00E-06 3.50E-06 4.00E-06 4.50E-06

50 60 70 80 90 100

PercentileCancer risk 4000
6000
8000
10000

(c) (d) Figure 4-2. Comparison of diffe rent numbers of iterations for tetrachloroethylene (most volatile).

4.2.2 Model Uncertainty Models used in this analysis were select ed based on science, policy, and professional judgment of the technical staff at EPA and RTI. In general, we chose the models based on previous use in similar regulatory decision ma king, together with the level of sc rutiny that they had previously received, including independent pe er review by subject matter expe rts, the EPA Science Advisory Board, and the Office of Management and B udget. For example, the groundwater model (EPACMTP) has been used by the Office of Res ource Conservation and Recovery (formerly the Office of Solid Waste) for over a decade in performing chemical risk assessments, and has been extensively peer reviewed and evaluated by EP A’s Science Advisory Board. In addition, we developed technical criteri a with which to evaluate our modeli ng options. For instance, none of the landfill models that we evaluated were capable of simulating the effects of landfill gas on transport and, given the properties of the chemical consti tuents, we determined that the landfill model must be able to simulate this process. Thus, we deve loped the LFCR specifically for this analysis and engaged several subject matter experts as advisors during the model development and parameterization. Regardless of the level of peer review and validation efforts, a ll of the models used in this risk analysis are subject to some level of model uncertainty becau se: (1) mathematical expressions used to represent complex fate and transport processes are simplifications of reality and (b) even simple models like the exposure model implicitly assume that individual behavior will result in a non-zero exposure if the contact medium is contam inated (e.g., receptor wells are assumed to be

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present at every landfill site a nd accessible to receptors). Naturall y, the predictive ability of the models depends both on how well we understand th e key processes, our ability to develop a reasonable mathematical construct of these proc esses, and the availabi lity of data on input parameters. In most cases, we simply do not have sufficient information to validate the models with field studies that span the full range of environmenta l conditions; as a result, we accept the fact that the use of computer models—regar dless of how thoroughly they are te sted—is associated with some level of uncertainty. Although we employ a variety of methods ra nging from mass balance checks to literature reviews, it is not possible to further reduce the model uncertainty without rigorous field testing and sampling. For example, fluxes from th e source model (air and leachate emissions) were averaged over the total surface ar ea of the landfill, even though the landfill’s area increases over time until all landfill cells are covered with waste. Th is averaging provides compatibility with the air and groundwater transport models, which do not allow for an increasing source area over time. Recognizing that this is a si mplification needed to allow th e models to “communicate,” we implemented several checks to ensure that the overall solvent mass balance is maintained. This represents a modeling uncertainty that could be addressed by revi sing the downstream models (e.g., EPACMTP); an option that would require considerable time and resources.

In the remainder of our discussion on model uncertainty, we focus on several key issues— several raised by peer reviewers—that should be acknowledged when interpre ting the results of the risk analysis.

4.2.2.1 Landfill Source Model The LFCR model is based on a relatively simple conceptualization of landfill processes. For this national-scale risk analysis, site-specific data were not available; consequently, we carefully considered the practical implica tions of using site-based (e.g., regional) and representative (e.g., landfill characteristics) data to run the simulations. A more detailed, mechanistic model would have been difficult (or impossible) to parameterize an d probably not feasible given the computational demands for this national analysis. Although the model strikes a reasonab le balance among model complexity, precision, data availability, and implem entability, one of the peer reviewers pointed out that ignoring leachate handling could represent a significant deficiency in our source modeling approach. In modern landfills, recovered leachate is typi cally managed in leachate collection systems. Some landfills send the recovered leachate to a tr eatment plant (municipal or other) while, in other cases, leachate is stored and often degraded in aerated surface impoundment s. Aerobic degradation could release volatile chemicals to the air, a scenario that is not currently re presented as part of the source modeling. Including this mass transfer woul d have required the inclusion of a surface impoundment source model, which was considered to be outside of the scope of this analysis. However, to address the peer reviewer’s concerns , we evaluated the potential significance of this pathway 20 by considering how the air pathway risk s and corresponding mass loading rates would change if all leachate mass volatilized (a highly conservative assumption).

Table 4-8 presents the minimum mass loading rate s for the air and groundwater pathways that meet the risk criteria; these are the mass loadi ng rates associated with the greatest estimated risk for the air and groundwater pathways, respectively, at the 90th percent ile of the risk distribution. The table also includes an estimate of the mass loading ra te associated with the ai r pathway if all leachate

20 Note that the leachate handling issue is of concern only for th e composite liner scenario, because leachate is not collected i n an unlined landfill.

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Do not cite or quote. 4-18 mass volatilized. This estimate is based on the Mass Loss Ratio, also shown in the table, which is the ratio between the average cumula tive total mass lost to the atmo sphere plus 100 percent of the average cumulative total leachate mass loss divided by the average cumulative total mass lost to the atmosphere (i.e., excluding the leachate mass). Acco rdingly, a Mass Loss Ratio close to 1 indicates that a much greater portion of the mass left the landfill through the air pathway versus the leachate pathway. The Mass Loss Ratio increases as the proportion of mass lost vi a the leachate pathway increases. Because mass is conserved and the mass loading rate is linear with respect to model outputs, we can use the Mass Loss Ra tio to estimate an allowable mass loading rate at the 90th percentile as though all of the leachate mass volatilized into the air. Thus, the adjusted mass loading rates in the table are calculated simply by dividing the minimum air pathway mass loading rate by the Mass Loss Ratio. The smallest mass loading rate for a chemical among all pathways is the mass loading limit (MLL), which is based on the greatest estimated risk (see Section 5 ). In all cases except methyl ethyl ketone, the groundwater pathway is associated with the MLL. If 100% of th e leachate mass is added to the air pathway, the MLL would change for only four chemicals (2-ethoxyethanol, acetone, methyl ethyl ketone, methyl isobut yl ketone). For all but one of these chemicals (2-ethoxyethanol), the adjusted MLL exceeds the solubility limit (see Section 5 ). If a mass loading rate exceeds the solubility limit, the solubility limit is used. Therefore, the final MLL would only change for one chemical (2-ethoxyethanol) if al l leachate mass were added to the air pathway. However, the assumption that 100 percent of the so lvent volatilizes to air is highly conservative and, therefore, the result is really a bounding estimate of the change in mass loading rates. Becau se the volatility of 2- ethoxyethanol is relatively low (t he least volatile chemical c onsidered), we would expect significantly less than 10 0 percent of the solvent to volatili ze from an impoundment. Moreover, biodegradation accounts for greater than 97 perc ent of the mass loss from the landfill for 2- ethoxyethanol (see Table 3-5). Based on the low volat ility and high biodegradat ion potential for this solvents, it is highly unlikely that including air emissions associated with leachate handling would result in a significant cha nge (if any) to the MLL.Review DraftSection 4.0 Risk CharacterizationDo not cite or quote.4-19Table 4-8. Minimum Mass Loading Rates forAir Pathway Adjusted to Include Leachate Mass (Composite Liner Scenario)Minimum Mass Loading Rates(kg/yr)Adjusted Mass Loading Ratesd(kg/yr)ChemicalaHLCb(atm-m3/mol)Kocc(L/kg)Air PathwayGroundwaterPathwayAverage MassLoss RatiodAir Pathway (with leachate mass)2-Ethoxyethanol1.2E-071.6E+017.6E+088.1E+075.9E+011.3E+07Cresols (total)9.5E-073.5E+016.7E+08e1.4E+061.8E+013.8E+07Methanol4.6E-069.0E+004.7E+081.2E+076.8E+006.8E+07Acetone3.9E-051.0E+004.4E+08e3.6E+08e1.6E+002.7E+08eMethyl ethyl ketone5.6E-059.3E-016.0E+07e1.9E+08e1.4E+004.4E+07eMethyl isobutyl ketone1.4E-047.4E+005.8E+07e5.6E+07e1.2E+005.0E+07e1,1,2-Trichloroethane8.2E-041.5E+024.1E+04f1.1E+04f1.0E+003.9E+04fDichlorobenzene, 1,2-1.9E-032.8E+022.0E+061.4E+061.0E+001.9E+06Methylene chloride2.2E-038.5E+002.4E+05f1.4E+06f1.0E+002.4E+05fChlorobenzene3.7E-031.6E+022.1E+052.6E+041.0E+002.1E+05Benzene (high)g5.5E-036.3E+011.6E+04f3.6E+03f1.0E+001.6E+04fBenzene (low)g5.5E-036.3E+015.9E+04f1.3E+04f1.0E+005.9E+04fToluene6.6E-032.7E+022.1E+07e6.1E+051.0E+002.1E+07eXylene (mixed isomers)6.7E-031.2E+031.2E+065.4E+051.0E+001.2E+06Ethyl benzene7.9E-035.2E+025.5E+062.6E+061.0E+005.5E+06Trichloroethylene1.0E-021.0E+026.1E+04f1.4E+04f1.0E+006.1E+04fTetrachloroethylene1.8E-022.2E+022.1E+04f9.5E+02f1.0E+002.1E+04faChemicals are sorted from least to most volatile. Butanol, cyclohexanone, ethyl acetate, and isobutanol are not shown becausethey have no inhalation risk benchmarks, thusthere are no calculated air pathway risks.bHLC is the Henry’s Law Constant (normalized at 25oC), a measure of the solubility of gases.cKoc is the organic carbon partition coefficient, a measure of the partitioning between water and solid phases.dThe Average Mass Loss Ratio is calculated from average cumulative total mass losses as follows: (air pathway + leachate pathway)/air pathway.eThe mass loading rate exceeds the solubility limit; however, forpurposes of the present calculation, raw mass loading rates were used rather than substituting the solubilitybased values (seeSection 5for discussion of solubility based limits).fBased on a cancer risk of 10-5. The Mass Loss Ratio is very close to 1 for all carcinogens considered here. Therefore, the minimum mass loading rate would remain relativelyunchanged if all leachate mass wereadded to the air pathway even if an alternative cancer risk threshold were selected (10-4or 10-6).ghigh = high-end benzene cancer health benchmark and low = low-end benzene cancer health benchmark.Review Draft Section 4.0 Risk Characterization

4.2.2.2 Air Dispersion Model

The Human Health Risk Prot ocol for Hazardous Waste Combustion Facilities (U.S. EPA, 2005c) provides the following discu ssion on some of the uncertainty in volved in the air dispersion modeling component of the risk analysis process: “Although dispersion modeling is a valuable tool for estimating concentration and deposition impacts, it has many limitations. Th e accuracy of the models is limited by

 The ability of the model algorithms to de pict atmospheric tran sport and dispersion of contaminants, and

 The accuracy and validity of the input data.

For example, most refined models require i nput of representative meteorological data from a single measuring station. In realit y, a release will encounter highly variable meteorological conditions that are consta ntly changing as it moves downwind. U.S. EPA’s Guideline on Air Quality Models—Revised (U.S. EPA 1986a, 1993b; Title 51 CFR Appendix W) describes two types of model uncertainty. Inherent uncertainty involves deviations in concentrations that occur even if all of the model input is accurate. Reducible uncertainty is associated with the model and the uncertain input values that will affect the results. Alt hough it is important to accurately represent actual conditions by selecting the right model, and using accurate and representative input data, all model results are subject to uncertainty. Nevertheless, models are generally considered reasona bly reliable in estimating the magnitude of highest concentrations resulting from a release, although they may not necessarily be timeand space-specific (Title 51 CFR Appe ndix W). When applied properly, air dispersion models are typically accurate to ± 10 to 40 percent and can be used to yield a “best estimate” of air concentratio ns (Title 51 CFR Appendix W).”

To capture the variability in meteorologi cal conditions across the United States, air dispersion modeling was conducted us ing data sets from meteorologi cal stations located within 57 “air regions” across the lower 48 states. Because dispersion also depends on the footprint of the source, we addressed variability in the landfill surface areas by c hoosing 19 representative areas to represent the distribution of landfills. Appendix G explains how linear interpolation was used to estimate unitized air concentrations fo r the actual landfills; we also pr esent an analysis that evaluates how well we achieved our goal of less than 5 percent interpolation error. The choice of receptor locations (discrete points located at various distances and directions from the landfill) is considered a major source of uncertainty in the air modeling simulations because (1) the distribution of actual resident locations around each site is unknown and (2) individuals move through time and space. We adopted a receptor grid approach that was developed to support previous listing determinations and risk assessme nt studies approximately 8 years ago. 21 This approach places the receptor points at concen tric distances at 25, 50, 75, 150, 300, 500, 1000 and 2000 meters from the edge of the landfill at 10° radial incremen ts around the full 360°. This approach tends to be conservative with respect to risk b ecause the ring distances are much cl oser together near the edge of the landfill; as a result, a high proportion of the receptor locations are closer to the source of

21 For example, the IWAIR model and Air Charac teristics Study finalized in 2002 included 6 receptor distances from the unit: 25, 5 0, 75, 150, 500, 1000 meters; this is very similar to the distances used in the Dyes and Pigments Listing Determination (U.S. EPA, 2003b).

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emissions where exposure concentrations in ambient ai r would be expected to be higher relative to outer distance rings. The approach used in locating receptor points for the ambient air exposure pathway is different than the approach used for the groundw ater pathway. For groundw ater exposures, the receptor locations (actually, the receptor wells) ar e located randomly throughout the lateral extent of the plume up to a distance of 1 mile. An analogous a pproach for the air pathway would have been to locate receptors within the dominant air plume; however, unlike groundwater flow, the prevailing wind at a given site varies widely in terms of both direction and st rength, making such an approach impractical, at best. To explore the uncertainty associated with re ceptor location for the ambient air pathway, we constructed an air modeling scenario that located receptors in a manner resembling the groundwater pathway. For each landfill site, we identified the point of maximu m air concentration (from among the distances and directions modeled) and assumed that this radial spoke was th e centerline of the air plume. We further assumed that the highest air concentrations w ould be found at 10° increments on either side of the centerline, thereby creating a 20 ° air “wedge” that consisted of receptor locations along 3 radial spokes and 8 distan ces (i.e., 24 receptor lo cations per wedge per site). Finally, we randomly sampled air concentrations at these rece ptor locations across all wedges and all sites for the most volatile and least volatile solvents with health benchmarks for th e inhalation pathway (i.e., tetrachloroethylene, 2-ethoxyethanol). At the 90th percentil e of the resulting risk distributions, we found that the risks were less than a factor of 2 higher than the risks predicted using radials and distances along the full 360°. Given th e fact that the close spacing of the rings nearest the landfill introduces a conservative bias, the results for the “wedge” scenario suggest that the air pathway methodology is appropriately conservative (i.e., does not underestimate risk).

4.2.2.3 Groundwater Model The groundwater pathway model, EPACMTP, assu mes that the soil and aquifer are uniform porous media. EPACMTP does not m odel preferential pathways such as fractures, macropores, or facilitated transport, which may affect migration of strongly sorbing const ituents such as metals. EPACMTP does not model colloidal transport or th e geochemical interactions between different contaminants in the leachate. A ny of these factors could result in under predicting contaminant concentrations at the receptor well. Conversel y, the EPACMTP model incorporates the following assumptions:

 Transverse dispersion is negligible in the uns aturated zone, potential ly resulting in an overestimation of risks

 Receptors use the uppermost aquifer, rather than a deeper aquifer, as a domestic source of drinking water, which overestimates risks where the uppermost aquifer is not used

 Hydrogeologic conditions that in fluence contaminant fate and tr ansport are uniform spatially as well as temporally (that is, in the time period over which the model is executed, 10,000 years), potentially resulting in an underest imation or overestimation of receptor well concentrations.

In modeling the fate and transport of ch emicals in groundwater, complex hydrogeologic environments, such as karst or highly fractured aquifers , were not assessed. Some fraction of the groundwater settings in this anal ysis may have fractured flow. In general, fractured flow in groundwater can channel the contaminant plume, thus allowing it to move faster and in a more

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concentrated state than in a non- fractured flow environment. As a result, the modeling may under- or overestimate the concentrat ions in the groundwater. Finally, there is uncertainty in predicting the movement of co ntaminants over long periods of time. The risk to receptors for the groundwater pathway was evaluated over a time period of 10,000 years. There are significant unc ertainties concerning how exposure and environmental assumptions will change over time, and the modeling methodology does not change these assumptions over this 10,000-year period. It should be pointed out that EPACMTP, its predecessors, and its de rivatives have been verified extensively during the past decade at each of the developmental stages. The model has been verified, in numerous cases, by comparing the simulation results against both analytical and numerical solutions. Additionally, EPACMTP and its pr edecessors have been validated using actual site data from four different sites. The relevant verification and validation history is discussed in detail in Appendix D of the EPACMTP Techni cal Background Document (U.S. EPA, 2003c). A brief summary of the verification history follows:

 The preliminary verification of EPACMTP was performed by U.S. EPA Office of Research and Development in 1992.

 Following the preliminary verification, detail ed module-level verification was conducted on the flow and transport sub-modules of the va dose-zone and the aquifer modules during the period 1993 to 1994. The modules were verified ag ainst analytical solutions, and numerical solutions from a number of well-documented simulators.

 In 1997, the EPACMTP code was verified utiliz ing a testing plan de veloped according to ASTM standards. The vadose-zone and the aqui fer modules, as well as the composite model (based on the sequentially linked vadose-zone an d aquifer modules), were verified against analytical and numerical solutions.

 In 1999, the vadose-zone and aquifer modules were extracted from EPACMTP to be included as part of the multimedia, multi-r eceptor, and multi-pathway risk assessment (3MRA) software system. The flow and transpor t sub-modules of both modules were verified against the results from EPACMTP.

 A pseudo-3D aquifer module was developed for the 3MRA software system. An exhaustive verification was conducted of all of the compone nts in the extracted vadose-zone module and the new pseudo-3D aquifer module in 2000. The m odules were verified against analytical solutions and EPACMTP results.

4.2.2.4 Exposure Model The Exposure Model calculates the drinking wa ter ADD as a function of several exposure factors, including body weight, consum ption of drinking water, and expo sure duration that vary with age. This is a very simple construct used by many programs at EPA (e.g., Superfund; Office of Water) to estimate dose assuming a constant exposure point concentration. Receptors are implicitly assumed to spend all of their time at the receptor point locations, introducing a conservative bias into the risk results. Similarly, the dermal absorbed dose (DAD) is calculated to estimate dermal dose as a function of exposure factors, including the ratio of skin surface area availabl e for contact (SA) to body weight, event duration, and exposure dur ation. A key assumption made in our DAD

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calculations was that 100 percent of the skin surface area was available for dermal contact. This assumption will in most cases result in an overest imation of dose. RAGS Pa rt E (U.S. EPA, 2004b), provides an overview of the uncerta inties associated with the dermal exposure analysis process. In this discussion, general statements are made about th e uncertainty associated with key parameters. In general, it is expected that the human exposure parameters used in the DAD calculation will be associated with low or medium uncertainty. The va lues used in our analysis were based on EFH and Child-Specific EFH data and are discussed in Section 4.2.3.8 . One key input parameter that is identified as being associated with a high level of uncertainty is the chemical-specific permeability coefficient (i.e., Kp term used in the calculation of DA_event). This parameter and its contribution to uncertainty in a dermal analysis are discussed in Section 4.2.3.6

.

4.2.2.5 Risk Model The Risk Model essentially compares the pred icted dose or air concentration to a human health benchmark, represented as a point estimate. Although the health benchmarks are derived with some consideration of intraspecies variability in toxicological sensitivity, the Risk Model does not explicitly simulate these differences. Given the diffe rences in child sensitivity to chemicals and the emerging research on latency of effe cts, the lack of child-specific tr eatment of health effects should be recognized as a limitation of the risk model.

4.2.3 Parameter Uncertainty As described in the Problem Formulation, the Monte Carlo approach was designed to capture the variability in landfill characteristics and envi ronmental setting across th e United States. To that end, we identified and developed distributions for most of the parameters required by the models. However, despite the significant effort involved in developing the input data, parameter values are associated with varying degrees of uncertainty. Uncer tainty in parameter estimates refers to a variety sources including, for example, (1) random or system atic measurement errors such as errors in measuring the octanol-water partition coefficient; (2) using surrogate data to represent actual parameter values such as using so il half lives as a measure of th e anaerobic biodegradation potential; (3) the representativeness of the data such as us ing a constructed data set to represent reasonable landfill characteristics rather than using actual landfill data; and (4) the variability in parameters that might change over time, space, or some population of individuals such as the variability in toxicological response. In addition, there can be significant uncertainties associated with the state of knowledge regarding data dependency because we are unable to identify and/or quantify relationships between and among input parame ters that are correlated in some way. In particular, the spatial and temporal variab ility in parameters relevant to predicted exposures has a significant influence on the distri bution of exposure and, ultimately, risks estimated for individuals. For example, the rainfall or prec ipitation rates used to calculate infiltration and recharge to groundwater are measured daily by th e National Weather Service at many locations throughout the United States; statistics about these parameters ar e well documented. Although the distributions of these parameters may be well known, their actual valu es vary spatially and temporally and cannot be predicted exactly. Thus, the annual average infiltra tion rates used in the LFCR model for a particular clim ate station will provide inform ation on average (not actual) conditions appropriate for this analysis. In addition, using data from multiple climate stations located throughout the United States can account for some, but not all, spatial variability.

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The remainder of this section describes how pa rameter variability (i.e ., Type A uncertainty) was addressed in the analysis, and discusses the pot ential implications of this uncertainty for the analysis results.

4.2.3.1 Climate Parameters The EPACMTP data system provided infiltrati on and liner leakage rate estimates. As discussed in Sections 2 and 3 , EPACMTP applies a site-based, regional Monte Carlo system to represent variability within and between landfills in the United States. This approach has been well documented, peer reviewed, and used in many risk analysis applications (e.g., U.S. EPA, 2003b). The general range of infiltra tion estimates (Table F-5 in Appendix F ) compares well with other landfill recharge estimates (e.g., Bartelt-Hunt et al ., 2006); however, the degree of uncertainty in the estimates cannot be characterized quantitatively without more extensive data. Model uncertainty exists with respect to the HELP model used to estimate infiltration rates within EPACMTP. However, the HELP model is well establishe d, and the EPACMTP parameterization is well documented and rigorous. The EPACMTP liner leak age rates are based on empirical data (Tetra Tech, 2001). The underlying data set is relatively small, resulting in greater uncertainty in the estimates. Nevertheless, no known bias exists in the EPACMTP infiltration or leaching rate estimates.

4.2.3.2 Cover/Liner Characterization Parameters Parameters characterizing the landfill cover and liner include soil moisture content, porosity, thickness, and geomembrane permeation coefficien t. The EPACMTP data system provided soil moisture content and porosity estimates. As described in Section 3.1.2.2 , EPACMTP incorporates a distribution of three soil types to represent national variability in soil conditions. The resulting ranges for moisture content (0.19–0.34 volume wa ter/volume total) and porosity (0.41–0.45) compare well with those from other sources (e.g., Benson et al., 1999; Barte lt-Hunt et al., 2006). The uncertainty associated with the EPACMTP soil parameter estimates cannot be evaluated quantitatively without more exte nsive data; however there is no known bias in the estimates.

The municipal landfill cover and liner thickne sses were based on S ubtitle D regulatory requirements. Geomembrane permeation coefficients were based on laboratory-measured values and estimates from a published, empirical correlation. Give n the range of literature values, the reported dependence on concentration, and the estimation from an empirical co rrelation, geomembrane permeation coefficients are uncerta in, leading to uncertainty in the degree of diffusion through composite liners. However, there is no known bias in the estimates. Sorption to cover/liner soil layers can be de scribed in the LFCR model by adjusting the diffusion coefficient (specificall y, dividing the diffusion coeffici ent by a retardation factor). However, for this analysis, it was assumed that so rption is inactive within cover/liner soil layers. This assumption is due to the absence of a generalized approach to estimate the retardation coefficient for national conditions, as well as sign ificant uncertainties and unresolved questions in the science of transport within cover/liner soils (see Section 3.1.1.2 ). Neglecting sorption in the cover/liner soil layers may provide a conservative estimate (overestimated mass loss). However, the extent of this potential bias is unknown given th e complexity and absence of a generalized modeling approach.

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The cover/liner characterizati on parameters influence the degree of diffusion through the liner or cover. Although some of the paramete rs are uncertain (e.g., geomembrane permeation coefficient), the relative mass loss via composite lin er leakage versus diffus ion in the results is consistent with results reported in the literature (Park et al. 1996; Edil, 2003; Foose 2002). In the LFCR results, diffusive transport through the line r exceeds leakage by up to three orders of magnitude. Several researchers have docum ented the importance of diffusion through geomembranes and emphasized that this transpor t mechanism can be much more significant than liner leakage for many VOCs (Park et al. 1996 ; Edil, 2003; Foose 2002). Park et al. (1996) demonstrated that the diffusive mass flux through a geomembrane can be more than two orders of magnitude greater than the mass flux due to the typical number and size of holes found in landfill geomembranes. The analysis in Foose (2002) demons trated that diffusive fluxes for many VOCs can be up to six orders of magnitude gr eater than the leakage. Therefore, the industrial solvents analysis result that diffusion through the geomembrane can exceed leakage by up to three orders of magnitude is consistent with the literature.

4.2.3.3 Hydrogeologic Setting Parameters The EPACMTP data system provided the hydrog eologic setting parameters, including the depth to groundwater and unsaturat ed zone soil moisture content and porosity. The development of correlated hydrogeologic environm ent data (e.g., unsaturated zone thickness, hydraulic conductivity) relied on a hydrogeologic database that included data from approxi mately 400 hazardous waste sites organized into 12 hydrogeologic environments. Each of the 66 Industrial D landfill locations was classified into one of the 12 hydrogeologic enviro nments, allowing associat ed correlated parameter values to be sampled. This appr oach assumed that these 12 hydr ogeologic environments and the associated correlated parameter distributions pr ovide an accurate representation of nationally variable landfill conditions. The uncertainty associated with these parameters is similar to the uncertainty described above for the EPACMTP-based climate and cover/liner parameters. These parameters influence the degree of diffusion to groundwater with no liner, wh ich accounts for a relatively small percentage of the mass loss, and only in the no-liner scenario (1 percent, on average). This fact coupled with the relative dominance of aqueous advective mass transf er to groundwater in the no liner scenario suggests that the uncertainty associ ated with these parameters has a relatively small impact on the results.

4.2.3.4 Landfill Characterization Parameters Landfill area, height, and operating life we re derived primarily from the EPA-WBJ database—the most up-to-date and complete resource available and identified for use in characterizing municipal la ndfills—as described in Section 3.1.2.4 . The resulting distributions compare well with other recent data sources (s ee Table 3-2), although some differences were apparent. The degree of uncertainty cannot be evaluated quantitativ ely, given the absence of more accurate and comprehensive landfill characteri zation data and, given the somewhat complex relationship among these parameters, understanding th e influence of individual parameters is not straightforward. For example, increasing the landfill area-to-height ratio (while holding other factors constant) increases the rate of mass loss (more surface area for transport per volume). However, greater depths are associated with more mass in the landfill and thus more mass release overall.

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Also, the operating life and volume contribute to the rate of mass release for a given landfill. Therefore, the influence of these parameters cannot be characterized simply without more extensive sensitivity analysis. Other landfill characterization parameters (landfill cell operating life, time before final cover, lift height, fraction of day wit hout a cover, working face angle) are associated with landfill management practices. The associated parameteriza tion relied strongly on c onsultation with experts in the field (Barlaz, 2006; Bartelt-Hunt, 2006). The value ranges, distributions , and correlations were designed to represent the population of municipal landfills in the c ontiguous United St ates, including the variability and relationships across these charac teristics. Lacking raw data to compare with the representative data, it was not feasible to quantify the uncertainty in the landfill characterization data set; however, there is no reason to expect that the re presentative data set created for this analysis contains any systematic bias.

4.2.3.5 Waste Characterization Parameters Waste characterization parameters include aver age waste temperature, bulk density, moisture content, porosity, fraction orga nic carbon, gas generation rate, and potential methane generation capacity. The waste characterization parameteri zation was based on literature review and consultation with experts in the field (Barla z, 2006; Bartelt-Hunt, 2006). The value ranges, distributions, and correlations were designed to provide a reasonable representation of the variability in these characteristics a nd the probable relationships between pa rameters (e.g., porosity is inversely correlated with bulk density). Although the uncertain ty cannot be evaluated quantitatively given the absence of more comprehensive data, no known bias ex ists in the waste characterization parameters.

4.2.3.6 Chemical Parameters Several chemical parameters influence sour ce model results, in cluding aqueous-phase diffusivity, vapor-phase diffusivit y, first-order biodegradation rate, first-order abiotic degradation rate (hydrolysis), Henry’s law constant, and organi c carbon partition coefficien t. The variability and uncertainty associated with these parameters is discussed below.

As described in Appendix B , estimation methods were used to calculate diffusivities from other chemical parameters (e.g., boiling point, cri tical temperature). The underlying parameters are typically based on laboratory measurements and re liable data sources. The estimation methods are well established. Therefore, the diffusivities are estimated with relatively less uncertainty. Appendix B describes the sources for first-order bi odegradation rate values. Biodegradation varies significantly given change s in such local conditi ons such as bacteria l populations, substrate characteristics (biodegradability of the wast e), temperature, and moisture. In addition, biodegradation rates measured in th e laboratory can differ significantl y from those measured in field conditions. The primary data sources for biodegr adation rates (e.g., Howard et al., 1991; Aronson and Howard, 1997) often relied strongly on laborato ry data, and the estimates were not developed specifically for landfills. Given these significant sour ces of variability and uncertainty, conservative values were selected for biode gradation rates (longe r half-lives). Given the significance of degradation for many of the chem icals analyzed (average mass percentage of approximately 41 percent, and a range from 0 to 99.3 percent), this fate mechanism contributes significantly to the uncertainty of the results.

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Abiotic degradation rates represented hydrolysis (see Appendix B ). Although some variability and uncertainty are asso ciated with this parameter, th e relative magnitude of mass loss associated with hydrolysis was insi gnificant relative to biotic degradation (abi otic degradation rates were orders of magnitude slower than biodegradation rates). Henry’s law constants and orga nic carbon partition coefficients were based on established literature values and subject to less variabil ity and uncertainty than other parameters. Chemical-specific permeability coefficients (Kp) are used in estimating dermal exposure through contact with contaminated groundwater. RAGS Part E (U.S. EPA, 2004b) states that permeability coefficients have been identified as a major contributor to parameter uncertainty in the analysis of dermal water exposure. In accordance with guidance in RAGS, we predicted Kp values based on chemical-specific log Kow and molecular we ight. This approach is recommended in RAGS for all organic chemicals to ensure consistency be tween Agency risk assessments in estimating the dermally absorbed dose from water exposures. For halogenated chemicals a nd other chemicals with large molecular weights, RAGS adds a note of cau tion regarding the estimation of Kp. Specifically, they note that Kp would be expect ed to be underestimated. For this reason, the Agency is exploring the use of a new Kp correlation based on molar volume and logKow.

4.2.3.7 Variability of Source Model Outputs

Section 3.1.3 (Tables 3-5 and 3-6) provides the rela tive percent contribution (of the total chemical mass added) for each of the primary transport pathways evaluated, summarized by chemical and liner scenario. These tables al so provide the maximum and minimum percent contributions for all of the simulations. Compari ng the range of percent contributions gives an indication of the variability associated with the parameters underlying the transport processes (e.g., degradation relies on the degradation rate; di ffusion relies on diffusivities and cover/liner characteristics). The most significant variability fo r all chemicals and liner scenarios is associated with degradation (a ra nge of contribution from 0 to 99.3 pe rcent, on average 41 percent). Other transport mechanisms (aqueous advection, vapo r advection, diffusion to atmosphere) exhibit significant variability, with ranges from 0 to greater than 90 percen t. However, vapor advection is the only mechanism other than degradation that contributes 10 percent or greater on average. Diffusion to groundwater exhibits somewhat less variability (0 to 56 pe rcent) and contributes relatively less to the overall mass loss (a pproximately 1 percent, on average). There are significant differences in results, depending on the specific chemical and physical properties of the constituent. For example, fo r cresols, the average mass loss through vapor advection is less than 0.1 percent, whereas for te trachloroethylene, the average mass loss is close to 70 percent through this pathway (the difference is primarily due to the much greater volatility of tetrachloroethylene). Although a formal sensitivit y analysis would provide more quantitative information regarding the contribut ion of different parameters to the variance in the results, these tables indicate which transport pathways are most significant and contribute the most variability to the source modeling results.

Do not cite or quote. 4-27Review Draft Section 4.0 Risk Characterization

4.2.3.8 Human Exposure Factors Exposure modeling relies heavily on default assumptions concerning population activity patterns, mobility, dietary habits, body weights, and other factors. Th e probabilistic analysis for the adult and child exposure scenario addressed the possible variabil ity in the exposure modeling by using statistical distributions for these variables for the adult and child re sidents. For all exposure factors varied, a single exposure f actor distribution was used for adults for both males and females. For child exposures, one age (age 1) was used to represent the age at the start of exposure, because this age group is considered to be most sensitive for most health effects.

The Exposure Factors Handbook (U.S. EPA, 1997c,d,e) provide s the current state of the science concerning exposure assu mptions, and it was used throughout this analysis to establish statistical distributions of values for each exposure parameter for each receptor. For most of the human exposure factors, distri butions were fitted to th e data summaries from the Exposure Factors Handbook or the Child-Specific Exposure Factors Handbook. The approach most often taken was to fit these distributions to selected percentiles rather than to the raw data. This approach implies that fitting to percentiles will produce essentially the same distribution as fitting to raw data. The chisquare goodness-of-fit test was used to select the mo st appropriate statistical distribution for most of the exposure factors. However, all of the statistics that were used to assess goodness of fit assume a random sample, which may or may not be a va lid assumption for data presented in the EFH’s
, because those data are based on surveys that, in many cases, do not involve purely random samples (e.g., they use clustering and stra tification, primarily for economic reasons). For a national Monte Carlo simulation, this is considered to be a relatively minor—albei t unquantified—source of uncertainty.

4.2.3.9 Human Health Benchmarks There are uncertainties inherent in the developm ent of the toxicity values, or human health benchmarks, used in this risk assessment. Uncertainties generally associated with human health benchmarks are discussed in detail in EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b), Methods for Derivation of Inhalation Refere nce Concentrations and Application of Inhalation Dosimetry (U.S. EPA, 1994a), and IRIS (U.S. EP A, 2007). Some uncertainties may be considered quantitatively, while others are generally expressed in qualitative terms. The reader may note that ”the primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures , including default options that are used in the absence of scientific data to the contrar y, should be health protective.” (U .S. EPA, 2005b Cancer Guidelines, pages 1-7).

Chronic noncancer reference valu es (RfCs and RfDs) represent chronic exposure levels that are intended to be health-protective levels. Specif ically, these values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (R fD) or of a continuous inhalation exposure (RfC) to the human population (i ncluding sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 22 To derive values that are intended to be “without appreciabl e risk,” the methodology relies on an uncertainty factor approach that includes consideration of both uncertainty and variability. When there are gaps in the available information, uncertainty factors are applied to de rive reference values th at are intended to be protective against appreciable risk of deleterious effects. Uncerta inty factors are commonly default

22 See IRIS glossary (www.epa. gov/NCEA/iris/help_gloss.htm).

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values 23 (e.g., factors of 10 or 3), used in the absence of compound- specific data; where data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed an d more uncertainty factors are used. Thus, there may be a greater tendency to overestimate ris k, in the sense that further study might support development of reference values th at are higher (i.e., less potent) because fewer default assumptions are needed. However, for some pollutants, it is possible that risks may be underestimated. While collectively termed “uncertainty factors,” these factors account for a number of different quantitative consideratio ns when using observed animal (usu ally rodent) or human toxicity data in the development of the reference concen tration. The uncertainty factors are intended to account for (1) variation in susceptibility among the members of the human population (i.e., interindividual variability); (2) uncerta inty in extrapolating from expe rimental animal data to humans (i.e., interspecies differences); (3 ) uncertainty in extrapolating fr om data obtained in a study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating the observed data to obtain an estimate of the e xposure associated with no adverse effects; and (5) uncertainty when the database is incomplete or there are problems with the applicability of available studies. Cancer unit risk estimate (URE) values used ar e those that have been developed to generally provide an upper-bound estimate of ri sk. That is, they represent a “p lausible upper limit to the true value of a quantity” (although th is is usually not a true statistical c onfidence limit). 24 In some circumstances, the true risk coul d be as low as zero; however, in other circumstances, the risk could also be greater. 25 When developing an upper-bound estimate of risk and to provide risk values that do not underestimate risk, health-p rotective default approaches ar e generally used. EPA typically uses the upper-bound estimates rather than lower- bound or central tendency estimates in our risk assessments, an approach that can have limitations for other uses (e.g., priority setting or expected benefits analysis). The hierarchy of sources that was implemen ted for this analysis (U.S. EPA, 2003f) was based largely on the rigor of review that a benchmar k has received. In addition to the strength of the review process, confidence in human health benchm arks also can be associated with the date the benchmarks were derived (or last reviewed). As additional toxicological data become available, the database becomes more complete . Methodologies evolve over time, with improvements in existing methods (e.g., EPA’s 1994 RfC methodology) and th e development of new health benchmark practices (e.g., benchmark dose me thodology). Therefore, there is le ss confidence associated with older benchmarks. The EPA IRIS assessment for benzene provides a range of plausible un it risk estimates. For inhalation unit risk estimates, the hi gh end of the range is 7.8E-06 per μ g/m 3 and the low end of the range is 2.2E-06 per μ g/m 3 . For oral exposures, the cancer slope factors that are derived from these unit risk estimates range from 5.5E-02 per mg/kg-da y at the high end of th e range to 1.5E-02 per

23 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] opti ons are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to va rious elements of the risk-assessment process when the correct scientific model is unknow n or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessmen t policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, th e agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is no t underestimated (although defaults are not intend ed to overtly overestim ate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices, EPA/100/B-04/001 available at: http://www.epa.gov/osa/ pdfs/ratf-final.pdf

. 24 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).
25 An exception to this is the URE for benzene, which is considered to cover a range of values, each end of wh ich is considered t o be equally plausible, and which is ba sed on maximum likel ihood estimates.

Do not cite or quote. 4-29Review Draft Section 4.0 Risk Characterization
mg/kg-day at the low end of the range. This assess ment used both the high and low ends of the range for both inhalation and oral exposur es, and thus two separate estim ates are presented for benzene cancer risks.

Most health benchmarks used in this analysis were from IRIS. Human health benchmarks in IRIS have been subjected to ri gorous internal and external reviews and represent EPA-wide consensus human health risk information. However, some benchmarks in IRIS are quite dated (e.g., the RfD for n-butanol was derived in 1990). The Pr ovisional Peer-Reviewed Toxicity Values derived by the Superfund Technical Support Center have been peer reviewed, but ha ve not undergone EPA’s formal review process, and therefore, do not re present verified benchmarks. ATSDR and CalEPA human health benchmarks have been subject to expert scientific and public review; the derivation of these benchmarks is transparent and publicly availa ble. The health benchmar ks in HEAST have not undergone as rigorous a review as those in IRIS and do not represent EPA-wide consensus information. HEAST has not been updated since 1997 and may not provide the most current source of information on some contaminants. Although ev ery effort has been made to identify peerreviewed reference values for cancer and noncance r effects for the constituents, some constituents have no such values or lack values for certain ex posure routes. In those cases, an understatement of risk for these constituents is possible. Finally, it should also be noted th at the toxicologies of trichloroethylene and tetrachloroet hylene (perchloroethy lene) are in different stages of an ongoing EPA review process being conducted in cooperation with the National Academy of Sciences. For tetrachloroethylene, we used both high and low e nd values for the inhalation unit risk estimates, which resulted in two separate estimates for tetrac hloroethylene cancer risks. We used the CalEPA value (5.9E-06 per μ g/m 3 ) as a higher end risk fact or. For the lower end valu e we used an inhalation unit risk estimate (7.1E-7 per μ g/m 3 ) published by EPA’s Office of Pollution Prevention and Toxic Substances (U.S. EPA 1998) that was based on the same study (by the National Toxicology Program in 1986) as the CalEPA inhalation value. Chemical-specific health benchmarks were used for all constituents. However, the RfD for m- and o-cresol was used as a surrogate for the cr esol mixture assessed in the analysis; this is not thought to have introduced any significant uncer tainty. Human health benchmarks are not agespecific and were therefore applied to both chil d and adult receptors, thereby introducing some uncertainty. Although receptors are likely to be exposed to mixtures of solvents, noncancer hazard and cancer risk were assessed separately for each constituent. Additivity across chemicals was not considered in the risk model; neither was syne rgism or antagonism. However, for noncarcinogens, HQ estimates were combined for chemicals having RfDs and RfCs with ma tching endpoints and no portal-of-entry effects (benzene, cresols, and methyl ethyl ketone). The CalEPA RfC for mixed cresols was derived based on the sa me oral study as the IRIS RfD fo r o- and m-cresol (route-to-route extrapolation). The IRIS RfC and RfD for ben zene were based on the same inhalation study. The IRIS RfC and RfD for methyl ethyl ketone were based on separate, route-specific reproductive/developmental studies, demonstrating that the developing fetus is the target organ for methyl ethyl ketone regardless of administration route. Additivity across routes of exposure for the same chemical was assumed; however, greater than or less than full additi vity may occur (e.g., if a metabolism pathway is overwhelmed and more parent compound is excreted from the body unchanged). Noncancer hazard may, therefore, have been under- or overestimated. For carcinogens, cancer risks across groundwater expo sure pathways were combined for adults (i.e., ingestion of drinking water, dermal contact wi th groundwater, and inhalation of shower air) and children (i.e., ingestion of drinking water and de rmal contact with groundwater), ir respective of the type of cancer caused by each route of exposure, possibly introducing some uncertainty in the mass loading rates.

Do not cite or quote. 4-30Review Draft Section 4.0 Risk Characterization

In the case of dermal noncancer hazards and cancer risk, estimates were made using the unadjusted RfD and oral CSF benchmarks. This a ssumption of 100 percent ab sorption may result in an overestimation of human health impacts from dermal contact during a bathing or showering event. However, the dermal route of exposure can result in different patterns of distribution, metabolism, and excretion than occur from the oral route.

There are uncertainties inherent in both the human exposure factors and human health benchmarks used in this analysis. Uncertainty is introduced by using probability distribution exposure durations together with lifetime average daily dose and RfD/RfC values. Assuming that the population parameters (e.g., exposure duration, bo dy weight, consumption rates) underlying the derivation of the RfD/RfC values are consistent with the probability distributions for exposure duration used in the exposure analysis may intro duce further uncertainty. However, in keeping with EPA’s goal of protecting public hea lth and the environment, default assumptions are used to ensure that risk to chemicals is not unde restimated and thus health benchmarks are designed to account for uncertainty by not underestimating risk.

Do not cite or quote. 4-31Review Draft Section 5.0 Landfill Mass Loading Rates



## 5.0 Landfill Mass Loading Rates
The objective of the wipes risk assessment wa s to calculate chemical-specific mass loading limits (in kg solvent/yr) that reflect an annual quan tity of a constituent that can be placed in a landfill and be protective of human health. These mass loadi ng limits are defined as mass loadings that result in cancer risks at the specified threshold (1 in 100,000) and non cancer HQs no greater than 1 at specified percentiles of th e risk distribution (i.e., 50t h and 90th percentiles). This section presents the mass loading limit results , as well as a summary of the steps used to estimate the mass loading limits. Many of the calcula tions presented in this section rely on the linearity of the modeling system, whereby th e results (mass flux from source, receptor concentrations, and risks) are linea rly proportional to the input chemic al concentration in the waste. The linearity of the system allows the use of linea r scaling factors to adjust the inputs and outputs to calculate risk-based mass loading limits . These calculations are described in Section 5.1

. Section 5.2 presents the mass loading limits associated with each scenario analyzed for the 90th and 50th percentiles of the ri sk distribution. Each scenario represents a specific chemical, receptor (adult, child), pathway (drinking water i ngestion, shower inhalation, ambient air inhalation), endpoint (cancer, noncancer), and liner t ype (no liner, composite liner).


### 5.1 Summary of Risk Analysis Calculations
The process used to calculat e the mass loading limits can be summarized by the following steps: 1. Using a constant initial concentration 26 for each solvent, we run the source model and produce a distribution of chemical releases (e.g., flux) to air and groundwat er that reflect the variability in landfill and environmental char acteristics across the nation. The source model simulation also produces 10,000 mass loading ra tes (MLRs) as function of the solvent concentration and the landfill characterist ics (e.g., landfill depth, surface area). 2. The risk metric for this analysis is a mass lo ading rate rather than a solvent concentration. Therefore, we normalized the outputs from the source model to a constant value by multiplying the result for each realization by a normalization factor. The normalization factor is simply the unitized rate (1 kg/yr) divided by the actual MLR for that realizat ion. The source model is linear with respect to both concentr ation and MLR, and therefore, we produce a set of 10,000 source model results that are used as input for the groun dwater and air models for each solvent/scenario. The solvent releases predicted for each individual realization are based on a constant MLR of 1 kg/yr.

3. We then take the normalized source model re sults and run the simulation through to risk, which includes the fate and transport models, th e exposure models, and the risk model. For each scenario, the resulting distribution of risk is sort ed and we identify the risk (or hazard) result at selected percentiles of the dist ribution, notably the 90th and 50th. 4. For the 90th percentile risk, we calculate a scaling f actor to derive the MLR that corresponds exactly to the cancer risk values evaluated (1E-4, 1E-5, or 1E-6) and/or an HQ of 1. The scaling factor is simply the ratio of the risk/hazard cr iteria to the risk/hazard estimate at the 90th

26 Using a fixed concentration allowed us to take advantage of QA/QC routines that ar e built into the model; however, we could ha ve simply run the source model using a fixed mass loading rate.

Do not cite or quote. 5-1Review Draft Section 5.0 Landfill Mass Loading Rates
percentile (e.g., for cancer, this is the risk level specified by the risk criteria divided by the estimated risk). 5. We then multiply the constant MLR of 1 kg/yr by the scaling factor at the selected percentile. Because the MLR is a constant value of 1 kg/yr, th e scaling factor is equivalent to the MLR at the percentile of interest (i.e., 1 x SF = SF = MLR). The MLR can be derived for any percentile of interest; however, the risk-based MLRs were derived at the 90th and 50th percentiles of the distribution per EPA policy for this analysis. 6. The lowest MLR represents that value that will protect at least 90 (or 50) percent of the hypothetical individuals (all recept or types) living within 2 km of a municipal solid waste landfill for all exposure pathways. As a check, these MLRs could be used as an input to the model to generate a distribution of risk estimates with the 90th and 50t h percentiles corresponding to a cancer risk level (1E-04, 1E-05, or 1E-06) or noncancer hazard level (HQ) of 1. This MLR is referred to as the Risk-Based Mass Loading Limit (RB-MLL) 7. Lastly, we perform a check on the MLR to ensu re that solubility limits are not exceeded (e.g., the MLR should not result in pure product in the landfill).

These steps are described in more detail in the following paragraphs.

Step 1. Run the Source Model Using a Constant Initial Concentration The landfill source model analysis included 10,0 00 simulations (realizations) per chemical and landfill liner scenario, which were designed to reflect the variability in national landfill conditions (e.g., climate, geometry, waste characterist ics). With 20 chemicals of interest and 2 liner scenarios, the source modeling included 400,000 si mulations. Each simulation produced a 200-year time series of annual mass loss to air and groundwater from the landfill. See Section 3.1 for further details about the source modeling.

The source model simulations used a constant initial solvent concentration in the landfill waste (1 kg solvent/m 3 waste). Because the simulated landfills have different volumes and operating life spans (to represent national vari ability), the chemical mass loading rate varies in the raw source model results. The simulation-specific mass loading rate was calculated as follows:

lf totin i T VC LR ×

= (Equation 5-1)

where

LR i = simulation-specific loading rate for i th simulation (kg/yr) C in = initial total chemical concentration in landfill waste (1 kg/m 3 ) V tot

= simulation-specific landfill volume (m 3 ) T lf

= simulation-specific landfill operating life (yr).

Step 2. Normalize the Source Model Resu lts to a Constant Mass Loading Rate Because the objective of the analysis was a ma ss loading limit rather than a concentration limit, the raw source model results were normalized to reflect a constant mass loading rate for all landfills. This normalization was accomplished by multiplying the source model results (mass loss per year to groundwater and air) by the followi ng simulation-specific no rmalization factor:

Do not cite or quote. 5-2Review Draft Section 5.0 Landfill Mass Loading Rates

i unit i LR LR NF = (Equation 5-2)

where

NF i = normalization factor for i th simulation (unitless) LR i = simulation-specific loading rate for i th simulation (kg/yr) LR unit = unit mass loading rate = 1 kg/yr.

With this adjustment, the source modeling results we re all based on a constant mass loading rate of 1 kg solvent/yr.

Step 3. Calculate Risks and Noncancer HQs Ba sed on the Unitized Mass Loading Rate

A distribution of risk estimates for each scen ario (solvent, liner type, contact medium, exposure route, receptor) was then calculated as described in Section 3 . Calculating risk from the source modeling results involves fate and transport modeling ( Section 3.2 , air dispersion modeling; Section 3.3 , groundwater modeling; and Section 3.4 , shower modeling), exposure modeling ( Section 3.5 ), and health effects modeling ( Section 3.6 ).

Step 4. Derive the Scaling Factors Needed to Calculate the Risk-Based Mass Loading Rates The scaling factor is the ratio of the risk or HQ criteria (i.e., risk of 1E-05 or HQ of 1) to the modeled risk or HQ for each simulation. This scali ng factor was then used to calculate the mass loading rate for each simulation:

i criteria
i Risk Risk SF = (Equation 5-3)

where

SF i = scaling factor for the i th simulation, used to calculat e mass loading rates (unitless) Risk criteria = risk or HQ criteria (1E-4, 1E-5, or 1E-6 for cancer or HQ = 1 for noncancer) (unitless) Risk i = modeled risk for the i th simulation (unitless).

Step 5. Calculate the Risk-Based Mass Loading Rates The mass loading rates for each simulation are equa l to the scaling factor values as shown in the following equation:

iiu LR SF LR nit = × (Equation 5-4)

where

LR i = mass loading rate for the i th simulation (kg/yr) SF i = scaling factor used to calc ulate mass loading rate for the i th simulation (unitless) LR unit = unit mass loading rate = 1 kg/yr.

This calculation relies on the fact that the source model results were normalized to a constant mass loading of 1 kg/yr. The results represent the risk-based mass lo ading rates for each simulation.

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Step 6. Select the Limiting Pathwa y for Selected Risk Percentiles The limiting pathway is the pathway/receptor/ endpoint combination (e.g., ingestion of drinking water/child receptor/cance r endpoint) with the smallest (mos t limiting) mass loading rate at the selected risk percentiles (i .e., 50th and 90th) for each of the 40 solvent/liner combinations. The resulting mass loading limit is a ssociated with the pathway/recepto r/endpoint combination with the highest risk for each solvent/liner combination. A ri sk-based mass loading rate at the risk criteria established by EPA (e.g., the 90th per centile of the dist ribution) is referred to as a Risk-Based Mass Loading Limit (RB-MLL).

Step 7. Check Whether the Mass Loading Limits Exceed Solvent Partitioning Limitations Because the model predicts extremely low leve ls of exposure and risk for a number of the solvents, some of the risk-based mass loading rates exceeded reasonable limits with respect to solvent partitioning in the landfill. Specifically, if the concentration of a chemical at the mass loading limit would on average exceed the solubility of that chemical, the result was flagged and an alternative solubility-based mass loading limit was used. The following equation was used to calculate the solubility-based mass loading limits:

avglf avgtot avgwsol sol T VRC LR

, , , × ×

= (Equation 5-5)

where

LR sol = solubility-based mass loading limit (kg/yr) C sol = chemical solubility (or density for chemicals miscible with water) (kg/ m 3 ) R w,avg = average solid-water partition coefficient, which accounts for equilibrium partitioning between the solid, aqueous, and air phases (unitless) V tot,avg = average landfill volume (m 3 ) T lf,avg = average landfill operating life (yr).

The solid-water partition coefficient, landfill volume, and landfill operating life vary by simulation based on estimated national variability; thus, average values are used for these parameters. For chemicals fully miscible with water, the chemical’s density was used in place of the solubility in the calculation above (at a concentration equal to the chemical’s density, the liquid would be pure chemical). If the risk-based mass loading limit exceed ed the solubility-based value, the result was flagged and the solubility-based mass loading limit was reported.


### 5.2 Mass Loading Limits

Table 5-1 shows the RB-MLLs based on noncancer e ffects at the 90th percentile of the distribution for each solvent and landfill liner co mbination calculated using the above steps. Table 5-2 shows the RB-MLLs based on noncancer effects at the 50th percentile. These tables also specify the limiting pathway (pathway/receptor) resulting in the maximum 90th and 50th percentile risks and the associated mass loading limits. Tables 5-3 and 5-4 present the RB-MLLs for chemicals with potential cancer effects. These tables compare the RB-MLLs for cancer risk thresholds of 1E-4, 1E5, and 1E-6, as well as a noncan cer risk threshold of HQ=1.

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Section 4.1 provides the 90th and 50th pe rcentile risk results for a ll pathways, receptors, and endpoints, and for each solvent and liner co mbination (not just the limiting pathway). Appendix A provides the results (risk and mass loading rates) for other percenti les (75th, 95th, and 99th). All of the risk values are associated with the constant unit mass loading rate of 1 kg/yr.

Table 5-1. RB-MLLs for Noncarcinogens at the 90th Percentile

No Liner Composite

CAS Chemical MLL (kg/yr) a Limiting Pathway b MLL (kg/yr) a Limiting Pathway b

79-00-5 1,1,2-Trichloroethane 1.2E+02 DW-Ch 1.0E+05 DW-Ch

110-80-5 2-Ethoxyethanol 2.3E+04 DW-Ch 8.1E+07 DW-Ch

67-64-1 Acetone 2.8E+04 DW-Ch 1.1E+08 S DW-Ch

71-43-2 Benzene 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A

71-36-3 Butanol 1.2E+04 DW-Ch 1.2E+07 DW-Ch

108-90-7 Chlorobenzene 5.8E+01 SH-A 2.6E+04 SH-A

1319-77-3 Cresols (total) 1.4E+03 Comb(DW)-Ch 1.4E+06 Comb(DW)-Ch

108-94-1 Cyclohexanone 9.9E+04 DW-Ch 7.5E+06 S DW-Ch

95-50-1 Dichlorobenzene, 1,2- 7.1E+02 SH-A 1.4E+06 SH-A

141-78-6 Ethyl acetate 5.3E+ 05 DW-Ch 4.4E +08 S DW-Ch

100-41-4 Ethyl benzene 2.1E+03 DW-Ch 2.6E+06 DW-Ch

78-83-1 Isobutanol 5.0E+04 DW-Ch 2.5E+07 S DW-Ch

67-56-1 Methanol 4.9E+03 DW-Ch 1.2E+07 DW-Ch

78-93-3 Methyl ethyl ketone 1.9E+04 Comb(DW)-Ch 3.0E+07 S AA-Ch

108-10-1 Methyl isobutyl ketone 6.2E+03 DW-Ch 1.4E+07 S DW-Ch

75-09-2 Methylene chloride 8.0E+02 SH-A 9.7E+05 AA-Ch

127-18-4 Tetrachloroethylene 1.3E+02 DW-Ch 5.6E+04 DW-Ch

108-88-3 Toluene 1.1E+03 DW-Ch 6.1E+05 DW-Ch

79-01-6 Trichloroethylene 7.3E+02 SH-A 4.4E+05 SH-A

1330-20-7 Xylene (mixed isomers) 4.8E+02 SH-A 5.4E+05 SH-A

a S = Based on solubility limit.

b Limiting Pathway is denoted as Pathway-Receptor, where:

AA = ambient air; Comb = combined drinking wate r, shower inhalation, shower/bath dermal; DM = shower/bath dermal; DW = drinking water; SH = shower inhalation. For combined (Comb) risks, the associated limiting pathway is provided in parentheses. A = Adult, Ch = Child.

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Table 5-2. RB-MLLs for Noncarcinogens at the 50th Percentile

No Liner Composite

CAS Chemical MLL (kg/yr) a Limiting Pathway b MLL (kg/yr) a Limiting Pathway b

79-00-5 1,1,2-Trichloroethane 4.8E+03 DW-Ch 5.9E+07 S NA

110-80-5 2-Ethoxyethanol 2.1E+06 DW-Ch 1.4E+09 S AA-Ch

67-64-1 Acetone 8.4E+05 DW-Ch 1.1E+08 S AA-Ch

71-43-2 Benzene 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch

71-36-3 Butanol 5.9E+05 DW-Ch 4.9E+08 S DW-Ch

108-90-7 Chlorobenzene 3.1E+03 SH-A 1.4E+06 AA-Ch

1319-77-3 Cresols (total) 9.9E+ 04 Comb(DW)-Ch 7.6E+07 S AA-Ch

108-94-1 Cyclohexanone 3.7E+06 DW-Ch 7.5E+06 S DW-Ch

95-50-1 Dichlorobenzene, 1,2- 4.2E+04 SH-A 4.0E+06 S AA-Ch

141-78-6 Ethyl acetate 4.4E +08 S DW-Ch 4.4E+08 S NA

100-41-4 Ethyl benzene 1.2E+05 DW-Ch 8.0E+06 S AA-Ch

78-83-1 Isobutanol 2.4E+06 DW-Ch 2.5E+07 S DW-Ch

67-56-1 Methanol 2.7E+05 DW-Ch 6.9E+08 S AA-Ch

78-93-3 Methyl ethyl ketone 8.0E+05 Comb(DW)-Ch 3.0E+07 S AA-Ch

108-10-1 Methyl isobutyl ketone 3.0E+05 DW-Ch 1.4E+07 S AA-Ch

75-09-2 Methylene chloride 3.4E+04 SH-A 6.4E+06 AA-Ch

127-18-4 Tetrachloroethylene 5.6E+03 DW-Ch 4.0E+06 S AA-Ch

108-88-3 Toluene 4.9E+04 DW-Ch 1.3E+07 S AA-Ch

79-01-6 Trichloroethylene 3.9E+04 SH-A 9.3E+06 AA-Ch

1330-20-7 Xylene (mixed isomers) 2.3E+04 SH-A 8.5E+06 AA-Ch

a S = Based on solubility limit.

b Limiting Pathway is denoted as Pathway-Receptor, where:

AA = ambient air; Comb = combined drinking wate r, shower inhalation, shower/bath dermal; DM = shower/bath dermal; DW = drinking water; SH = shower inhalation. For combined (Comb) risks, the associated limiting pathway is provided in parentheses. A = Adult, Ch = Child.

Do not cite or quote. 5-6Review Draft Section 5.0 Landfill Mass Loading Rates

Table 5-3. RB-MLLs for Carcinogens at the 90th Percentile

No Liner Composite

Risk Threshold MLL (kg/yr) a Limiting Pathway b MLL (kg/yr) a Limiting Pathway b

79-00-5: 1,1,2-Trichloroethane

10 -4 1.1E+02 Comb(SH)-A 1.1E+05 Comb(DW)-A

10 -5 1.1E+01 Comb(SH)-A 1.1E+04 Comb(DW)-A

10 -6 1.1E+00 Comb(SH)-A 1.1E+03 Comb(DW)-A HQ=1 1.2E+02 DW-Ch 1.0E+05 DW-Ch 71-43-2: Benzene (high) c

10 -4 5.0E+01 Comb(SH)-A 3.6E+04 Comb(SH)-A

10 -5 5.0E+00 Comb(SH)-A 3.6E+03 Comb(SH)-A

10 -6 5.0E-01 Comb(SH)-A 3.6E+02 Comb(SH)-A HQ=1 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A
71-43-2: Benzene (low) c

10 -4 1.8E+02 Comb(SH)-A 1.3E+05 Comb(SH)-A

10 -5 1.8E+01 Comb(SH)-A 1.3E+04 Comb(SH)-A

10 -6 1.8E+00 Comb(SH)-A 1.3E+03 Comb(SH)-A HQ=1 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A 75-09-2: Methylene Chloride

10 -4 1.1E+03 Comb(DW)-A 2.4E+06 AA-A

10 -5 1.1E+02 Comb(DW)-A 2.4E+05 AA-A

10 -6 1.1E+01 Comb(DW)-A 2.4E+04 AA-A HQ=1 8.0E+02 SH-A 9.7E+05 AA-Ch 127-18-4: Tetrachloroethylene (high) c

10 -4 1.7E+01 Comb(DW)-A 8.2E+03 Comb(DW)-A

10 -5 1.7E+00 Comb(DW)-A 8.2E+02 Comb(DW)-A

10 -6 1.7E-01 Comb(DW)-A 8.2E+01 Comb(DW)-A HQ=1 1.3E+02 DW-Ch 5.6E+04 DW-Ch 127-18-4: Tetrachloroethylene (low) c

10 -4 2.0E+01 Comb(DW)-A 9.8E+03 Comb(DW)-A

10 -5 2.0E+00 Comb(DW)-A 9.8E+02 Comb(DW)-A

10 -6 2.0E-01 Comb(DW)-A 9.8E+01 Comb(DW)-A HQ=1 1.3E+02 DW-Ch 5.6E+04 DW-Ch 79-01-6: Trichloroethylene

10 -4 2.2E+02 Comb(SH)-A 1.3E+05 Comb(DW)-A

10 -5 2.2E+01 Comb(SH)-A 1.3E+04 Comb(DW)-A

10 -6 2.2E+00 Comb(SH)-A 1.3E+03 Comb(DW)-A HQ=1 7.3E+02 SH-A 4.4E+05 SH-A

Do not cite or quote. 5-7Review Draft Section 5.0 Landfill Mass Loading Rates

a S = Based on solubility limit.

b Limiting Pathway is denoted as Pathway-Receptor, where:

AA = ambient air; Comb = combined drinking wate r, shower inhalation, shower/bath dermal; DM = shower/bath dermal; DW = drinking water; SH = shower inhalation. For combined (Comb) risks, the associated limiting pathway is provided in parentheses. A = Adult, Ch = Child.

c Two estimates are provided for benzen e and tetrachloroethylene cancer risks to reflect the high and low ends of

the range of benchmarks.

Do not cite or quote. 5-8Review Draft Section 5.0 Landfill Mass Loading Rates

Table 5-4. RB-MLLs for Carcinogens at the 50th Percentile

No Liner Composite

Risk Threshold MLL (kg/yr) a Limiting Pathway b MLL (kg/yr) a Limiting Pathway b

79-00-5: 1,1,2-Trichloroethane

10 -4 5.9E+03 Comb(SH)-A 3.8E+06 AA-A

10 -5 5.9E+02 Comb(SH)-A 3.8E+05 AA-A

10 -6 5.9E+01 Comb(SH)-A 3.8E+04 AA-A HQ=1 4.8E+03 DW-Ch 5.9E+07 S NA 71-43-2: Benzene (high) c

10 -4 2.9E+03 Comb(DW)-A 1.4E+06 AA-A

10 -5 2.9E+02 Comb(DW)-A 1.4E+05 AA-A

10 -6 2.9E+01 Comb(DW)-A 1.4E+04 AA-A HQ=1 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch
71-43-2: Benzene (low) c

10 -4 1.0E+04 Comb(DW)-A 4.8E+06 AA-A

10 -5 1.0E+03 Comb(DW)-A 4.8E+05 AA-A

10 -6 1.0E+02 Comb(DW)-A 4.8E+04 AA-A HQ=1 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch 75-09-2: Methylene Chloride

10 -4 5.4E+04 Comb(SH)-A 1.1E+07 S AA-A

10 -5 5.4E+03 Comb(SH)-A 2.2E+06 AA-A

10 -6 5.4E+02 Comb(SH)-A 2.2E+05 AA-A HQ=1 3.4E+04 SH-A 6.4E+06 AA-Ch 127-18-4: Tetrachloroethylene (high) c

10 -4 9.5E+02 Comb(DW)-A 1.7E+06 AA-A

10 -5 9.5E+01 Comb(DW)-A 1.7E+05 AA-A

10 -6 9.5E+00 Comb(DW)-A 1.7E+04 AA-A HQ=1 5.6E+03 DW-Ch 4.0E+06 S AA-Ch 127-18-4: Tetrachloroethylene (low) c

10 -4 1.1E+03 Comb(DW)-A 1.5E+07 AA-A

10 -5 1.1E+02 Comb(DW)-A 1.5E+06 AA-A

10 -6 1.1E+01 Comb(DW)-A 1.5E+05 AA-A HQ=1 5.6E+03 DW-Ch 4.0E+06 S AA-Ch
79-01-6: Trichloroethylene

10 -4 1.3E+04 Comb(SH)-A 5.1E+06 AA-A

10 -5 1.3E+03 Comb(SH)-A 5.1E+05 AA-A

10 -6 1.3E+02 Comb(SH)-A 5.1E+04 AA-A HQ=1 3.9E+04 SH-A 9.3E+06 AA-Ch

Do not cite or quote. 5-9Review Draft Section 5.0 Landfill Mass Loading Rates

a S = Based on solubility limit.

b Limiting Pathway is denoted as Pathway-Receptor, where:

AA = ambient air; Comb = combined drinking wate r, shower inhalation, shower/bath dermal; DM = shower/bath dermal; DW = drinking water; SH = shower inhalation. For combined (Comb) risks, the associated limiting pathway is provided in parentheses. A = Adult, Ch = Child.

c Two estimates are provided for benzen e and tetrachloroethylene cancer risks to reflect the high and low ends of

the range of benchmarks.

Do not cite or quote. 5-10Review Draft Section 6.0 References



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Do not cite or quote. 6-6Review Draft Section 6.0 References U.S. EPA (Environmental Protection Agency). 2005a. Landfill Gas Emissions Model (LandGEM) Version 3.02 User’s Guide . EPA-600/R-05/047. U.S. EPA, Office of Research and Development, Morrisville, NC. Available at http://www.epa.gov/ttncatc1/products.html

. U.S. EPA (Environmental Protection Agency). 2005b. Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001B. U.S. EPA, Risk Assessment Forum. March. Washington, DC. U.S. EPA (Environmental Protection Agency). 2005c. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. EPA/530/R-05/006. U.S. EP A, Office of Solid Waste and Emergency Response. Septem ber. Washington, DC. Available at http://www.epa.gov/waste/hazard/tsd/td/combust/risk.htm

. U.S. EPA (Environmental Protection Agency). 2006a. Landfill Methane Outreach Program Database (LMOP). http://www.epa.gov/lmop/index.htm. U.S. EPA, Office of Air and Radiation, Washington, DC. U.S. EPA (Environmental Protection Agency), 2006b. 2005 Urban Air Toxics Monitoring Program (UATMP) . EPA-454/R-07-001. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at http://www.epa.gov/ttnamti1/files/ ambient/airtox/2005_uatmp_main_full.pdf

. U.S. EPA (Environmental Protection Agency). 2006c. Child-Specific Exposure Factors Handbook (External Review Draft) . EPA-600/R-06-096a. U.S. EPA, National Center for Environmental Assessment, Cincinnati, OH. September. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56747. U.S. EPA (Environmental Protection Agency). 2006d. National Recommended Water Quality Criteria . Office of Water, Office of Science and Technology. 2006. U.S. EPA (Environmental Protection Agency). 2006e. National Analysis: The National Biennial RCRA Hazardous Waste Report (based on 2003 data). Available at http://www.epa.gov/osw/infores ources/data/br03/national03.pdf

. U.S. EPA (Environmental Protection Agency). 2007. Integrated Risk Info rmation System (IRIS)

. U.S. EPA, National Center for Environmen tal Assessment, Office of Research and Development, Washington, DC. Available at http://www.epa.gov/iris/

. U.S. EPA (Environmental Protection Agency). 2008. IRIS Toxicological Review of Tetrachloroethylene (Perchloroethylene) (External Review Draft) . U.S. Environmental Protection Agency, Washington, DC, EPA/635/R-08/011A.

Do not cite or quote. 6-7